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Tag: Pius Adesanmi
CALL FOR SUBMISSIONS Wreaths for a Wayfarer: A Poetry Anthology in Honour of Pius Adesanmi (1972-2019)
On Sunday, March 10, 2019, Pius Adebola Adesanmi, writer, scholar, educator, and public intellectual, was killed along with 156 others from 35 countries in the Ethiopian Airlines plane that crashed close to Addis Ababa airport shortly after take-off. Prior to his death, Payo (as he was fondly called by many) was the Director of the Institute of African Studies, Carleton University, in Ottawa, Canada. His tragic demise at the age of 47 has left people in the communities where he conducted his professional work and social activism reeling with pains. The tragic nature of his death invites us to reflect on his life and times, as well as to philosophize on the immortal lines of the English poet, John Donne, in his "Meditation XVII": "All mankind is of one author, and is one volume; when one man dies, one chapter is not torn out of the book, but translated into a better language; and every chapter must be so translated… As therefore the bell that rings to a sermon, calls not upon the preacher only, but upon the congregation to come: so this bell calls us all… No man is an island, entire of itself… any man's death diminishes me, because I am involved in mankind; and therefore never send to know for whom the bell tolls; it tolls for thee."
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Posted in LITERARY MISSIONARY, SOCIAL CRITIC
PIUS, NAIJA SCHOOLS AND THE LAGOS COLLAPSE
Only yesterday, it feels like that, Nigeria went into mourning over the death of Pius Adesanmi, our much beloved Professor of many letters and social critic. Our eyes are still wet with the tears.
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Posted in ESSAYS AND LITERARY JOURNEYS, LITERARY MISSIONARY, REVIEWS
Post-centenary Nigeria: New literatures, new leaders, new nation by Pius Adesanmi
Pius Adesanmi
I guess it's the water. Something must be in the water that members of Nigeria's literary fraternity and sorority drink which makes them see a direct connection between prosperity Pentecostalism – as we experience it in Nigeria today – and their own calling in the Republic of Letters. I do not need to tell anybody that Pentecostalism of the prosperity ilk owns the copyright to the narrative and actuality of miracles in the lives of the citizens of this country. Indeed, so gripping is the national preoccupation with miracles and portions as the immediate dividend of democracy, sorry, of faith, that our Muslim brethren are determined not to be left out of the scramble of each Nigerian to claim his portion and possess his possession. A video recently went viral on social media of a Muslim cleric in full public performance of the stage rituals of a prosperity Pentecostal pastor delivering miracles of healing and material reward. Miracle workers are the new cool and I am starting to have a nagging suspicion that folks believe that literature, like prosperity Pentecostalism, is a miracle worker.
Today, I am asked to address the theme of newness. As if this wasn't challenging enough as a theme, I've been saddled with the baggage of newness raised to power three: new literatures, new leaders, new nation in post-centenary Nigeria. In essence, I am asked to examine the possible ways in which our new literatures – however, defined and imagined – impact on leadership and nationhood. Let's just hope that the organizers of the Garden City Literary Festival will not ask me to come one day and address the linkages between literature and the provision of good roads, hospitals, and sundry infrastructure!
Now, the adjective "new" has been used to qualify three nouns in our topic, but we can only confidently bear witness to the truthfulness of one as far as Nigeria is concerned. New literatures? That is true, very much true in this country. New leaders? That's a lie, for we do not even have old leaders let alone new ones. New nation? That also is a lie, for we are more remarkable for our violations of every definition of nationhood than for a will to the emergence of a nation, new or old. These, of course, are contentious considerations that we shall return to in due course.
In essence, I am saying that those who, at the Uyo ANA Convention, in 2012, dreamed of the ability of literature to open up and probe new imaginaries of national security and those who in Minna have asked me to interrogate the ability of our new literatures to map new narratives of nationhood and leadership are writers who understand that the word and the story have been at the foundation of every human enterprise from the beginning of time. If we are inclined to go back as far as Ancient Greece and Rome, we will remind ourselves that the content, shape, nature, character, and identity of those two political expressions of civilization and human organization took their inchoate foundational steps in the narratives of the storyteller. Ancient Greece and Rome were not just imaginaries that became political realities, they were imagined and narrativized in epics, myths, legends, and even dramaturgy of the tragic ilk. Subscription to the interpellative power of these narratives is what confers on them legitimacy and hegemony. They acquire an amniotic essence of national histories and identity. The national epic is the most powerful source of identity for the modern nation and the nation-state.
Think of what Homer's Iliad means for Ancient Greece. Think of what Virgil's Aeneid means for Ancient Rome. Think of The Tale of Genji for Japan; of La Chanson de Roland for France; of the Epic of Gilgameshfor Iraq; of Ramayana for India; Beowulf to Anglo-Saxons and Britain; think of all these national epics and you come to an understanding of the fact that literature, indeed, has the miraculous power to forge the essence and the spirit of a nation – and of a nation-state depending on the level of legitimacy and hegemony it acquires across time. From Ernest Renan to Benedict Anderson, no scholar of nation, nation-state, and nationalism has ever made short shrift of the centrality of the creative imagination, of stories, of myths and legends to the emergence and enabling of the lineage of nation and nation-state.
That is why Benedict Anderson defined nation as an imagined community. "Literature is Fire", screams the Peruvian Nobel Laureate, Mario Vargas Llosa, in a 1967 lecture he presented on being awarded the Romulo Gallegos Literary Prize in Venezuela. Vargas Llosa agrees on the centrality of literature to the soul of nation and nation state in the said essay. Literature is fire! If literature is fire, what does this mean for those of us who are writers and workers in the field of creativity and imagination? It reminds us that literature is promethean. You will recall that the first time man is said to have stolen from the Gods – I would have called it man's first act of corruption but, luckily for us, stealing is not corruption in this country – what he stole was fire! Prometheus stole fire from the gods and gave man the miracle of creation and creativity. Literature is fire! Literature is miracle. Literature makes miracles!
Long before ANA National thought of the prophylactic powers of literary imagination, long before the conveners of this edition of the MBA International Literary Colloquium thought of a theme which connects literature to imaginaries of leadership and nationhood, generations of writers before us have imagined the connections between literature and project nationhood. It could not have been otherwise. At the political level, Nigeria may have been a product of colonial will, violence, and desire. She may have been an economic contraption cobbled together by Lord Lugard and named by Flora Shaw for the benefit of Great Britain. Nigeria's first generation of writers in the English language – the producers of what we now conventionally call modern African literatures – were not going to allow the original injury of our foundation – colonial dehumanization – to become the basis of postcolonial project nationhood.
Two factors aided the task of this generation in terms of imagining a postcolonial becoming for Nigeria. First is the fact that they were coming from an oral tradition in which art was indissociable from politics. The verbal arts, all our oral genres, served a utilitarian function in traditional society. Thus, the transition to a modern aesthetic of protest and resistance was not such a huge leap for our foundational writers in English. Second is the fact that these writers came of age in the climate of 20thcentury grand narratives of freedom. This was the age of Negritude, of pan-Africanism, of the African nationalist struggle, of decolonization. This was the age of the radical liberator which gave us such figures as Frantz Fanon, Walter Rodney, Amilcar Cabral, Eduardo Mondlane, Patrice Lumumba, Agostinho Neto. This was the age of the grand dreams and ambitions of Kwame Nkrumah, Julius Nyerere, Nnamdi Azikiwe, and all their contemporaries for the continent. Literature slipped into a predictable role as the cultural wing of this struggle.
Achebe served what I consider to be the most direct notice of his generation's mission to deploy art in the service of society and nation in his essayistic career. You are certainly familiar with essays such as "The Novelist as Teacher", and "The African Writer and His Society". You will recall his clear and oft-reiterated point in some of these essays that the writer cannot be expected to be excused from the task of nation building and other socio-political debts to his society. More than any transcendental commonality of themes, motifs, and textual strategies in their creative enterprise, I believe that this sense of a socio-political mission, of the need to press art into the service of new imaginaries of nationhood in which a culturally-empowered and psychologically-decolonized citizenry shall embrace the possibilities of postcolonial nationhood is what came to give these writers the first sense of a generational identity in the annals of Nigerian writing.
Wole Soyinka, for instance, often alludes to a "foundational quartet" of Nigerian letters comprising himself, Chinua Achebe, J.P.Clark, and Christopher Okigbo. We must bear it in mind that the imagination of this foundational quartet was present at the birthing of Nigeria's postcolonial moment. Their creative contribution to emergence of a new nation in those heady years came in the shape of visions and intimations of the roads to be taken, of roads to avoid, of pitfalls that could lead to anomie and errors of the rendering. Indeed, Soyinka heralded the birth of his new nation with a prophetic play, A Dance of the Forests; Achebe predicted corruption and coups in A Man of the People; Okigbo foretold wars and rumbles of war in Limits. By the time we arrive at J.P. Clark's lamentations of inertia, corruption, and the myriad dysfunctions of Nigerian statehood in his 1980s poetry collection, State of the Union, it was clear that Nigeria had made Cassandra of this generation of writers at prohibitive national costs.
Perhaps this is why Soyinka looked at his generation in the arts and beyond and famously or infamously described her as a wasted generation? He is not alone. The next set of writers to gain a loose collective consciousness as a generation also had a grandiose ambition and belief in the ability of literature to imagine and will into being a Nigerian nation that would work for a Nigerian people. These writers shook off post-independence disillusionment and disappointment to embark on a radical journey to regain the personal and the national selves. They had no patience with the eclectic theoretical and aesthetic sensibilities of the Soyinkas and the Achebes of this world. Theirs was going to be hotheaded and fire-spitting Marxism or Marx-influenced discursive and aesthetic radicalism. Think of Zaynab Alkali, Tanure Ojaide, Femi Osofisan, Bode Sowande, Niyi Osundare, Festus Iyayi, Tunde Fatunde, Kole Omotoso, Odia Ofeimun, Wale Okediran, Olu Obafemi, Ken Saro- Wiwa, Abubakar Gimba, Chidi Amuta, Stanley Macebuh, Chinweizu, Biodun Jeyifo and how they fired up the Nigerian literary scene in the 1970s and 1980s. be hotheaded and fire-spitting Marxism or Marx-influenced
Sometime in the early 1990s, Femi Osofisan presented a lecture at the University of Leeds which contained the report card of his generation in Nigeria – and elsewhere in Africa – as far as their lofty dreams of literature and nation, literature and society, were concerned. Soyinka spoke of a wasted generation, right? Well, Femi Osofisan's lecture is entitled "Warriors of a Failed Utopia". A certain Nigeria of a certain dream is, of course, the failed utopia captured in Osofisan's lamentation. By the time Osofisan was presenting his lecture, Nigeria had endured the military dictatorships of Buhari/Idiagbon, Ibrahim Babangida, and Sani Abacha. SAP had drained the soul of the Nigerian people and emptied the content of Nigerian nationhood. Corruption was eating up the crumbs left by SAP. The embrace of Marxism and poverty no longer appealed to this generation. Many moved abroad. Those who did not move abroad went in pursuit of capitalism and prosperity.
Wasted generation. Warriors of a failed utopia. Two writers, two generations, two successive grim report cards on Nigeria's stubborn resistance to the Aesculapian functions of literature. My generation should have taken a hint. We should have remembered the tale of the wise tortoise. The tortoise arrived at the entrance of the lion's cave, noticed that all the paw prints of the animals who had gone to pay a condolence visit to the sick king of the jungle were going inside and none was coming out, and took a life-saving hint. The tortoise did not enter the cave. My generation did. We entered the cave of national envisioning because that is what literature commands. I mentioned Mari Vargas Llosa's essay earlier. The crux of his submission in that essay comes down to this statement: "nobody who is satisfied can write." That is Vargas Llosa's one-sentence definition of the creative vocation. Literature is a permanent dissatisfaction with one's condition, one's environment, one's society, one's community. It is out of the cauldron of that dissatisfaction that literature dares to imagine differently. Art imagines out of a deontology of dissatisfaction. Ask John Lennon.
My generation entered that cave, because we were dissatisfied. Just as Soyinka and his foundational quartet were dissatisfied. Just as Femi Osofisan and his warriors of a failed utopia were dissatisfied. To my knowledge, no member of my generation has ever announced our report card the way Soyinka and Osofisan made definitive pronouncements on their respective generations. It could be because our story, our entanglements or lack thereof with project nationhood, is still unfolding. If we won't make that call, if we won't make that pronouncement, there are new kids on Nigeria's literary block. They have been rocking the Nigerian, African, and global literary worlds. These new kids on the block – more on them later – would make the call for my generation. They are that irreverent because they are good at what they do. They would announce the report card of generation in terms of literary accomplishment and in terms of what we did or did not do with the task of literature and nation.
The novelist and poet, Richard Ali, is a one of the new kids on the block. I love Richard Ali. Not in a way that will guarantee me a14-year prison term in Kirikiri. Richard Ali is what you would call my protégé. That is the sense in which I love him. But I am not afraid to say this: God soda Richard Ali's mouth! It was on Richard Ali's Facebook Wall that I first chanced upon a thread in which his generation was passing judgment on mine! Their judgment was harsh and unforgiving. Whereas Soyinka and Osofisan had described two generations of wasted or failed warriors in search of a failed Nigerian utopia, Richard Ali and his fellow coroners did a post-mortem on my generation and simply placed a question mark to indicate absence and emptiness where we had been or ought to be. Where are they? What happened to this generation, Richard Ali queried. I dare not tell you the discussion that ensued among members of Richard's generation as they took their koboko and went after us. Trust Deji Toye not to carry last in such a venture.
Nigeria happened to my generation. Incidentally, a poem I wrote about haemorrhage, about Nigeria bleeding us to Euro-America, included in my collection, The Wayfarer and Other Poems, was what prompted Richard Ali's reflection on my generation. We did not set out to be exiles. We did not set out to be the Andrew generation whose space is now occupied by Richard Ali's question mark. In 1988, Harry Garuba edited a volume of poetry whose significance remains unsurpassed in the annals of Nigerian letters as far as I am concerned. Voices from the Fringe did for Nigerian letters what Leopold Sedar Senghor's An Anthology of New Negro and Malagasy Poetry did for Negritude poetry back in 1948. Senghor's anthology announced the birth of Negritude poetry, Garuba's screamed the arrival of a new generation of Nigerian writers. The critic, Chris Dunton, and yours truly spent much of the 1990s and early 2000s theorizing these new creative effervescence as the handiwork of those we labelled Nigeria's third generation writers.
This generation did not remain long on the fringe. By the early 1990s, they were the only news in Nigerian literature. Today, those of us who move in Euro-American circuitries of knowledge production on African literatures often marvel at the sort of literary history that is always constructed for Nigeria abroad. You'd think that Nigerian literature went to sleep after the Osofisan generation concluded that they were warriors of a failed utopia and roared back to life only in the 2000s when Helon Habila, Chimamanda Adichie, Chris Abani, Helen Oyeyemi, Sefi Atta, Chika Unigwe, E.C Osondu, Uwem Akpan, Biyi Bandele, and Teju Cole came to global attention. Viewed from the global north, it is as if the literary effervescence of the late 1980s to the 1990s never happened.
Yet, the foundation of whatever is happening today in Nigerian letters can be traced to the renewal of creative juices and energies by the generation of Remi Raji, Wumi Raji, Sola Osofisan, Uche Nduka, Olu Oguibe, Obi Nwakanma, Okey Ndibe, Obi Iwuanyanwu, Obu Udeozor, Uzor Maxim Uzoatu, Maik Nwosu, Chiedu Ezeanah, Esiaba Irobi, Ogaga Ifowodo, Toyin Adewale, Omowumi Segun, Nike Adesuyi, Lola Shoneyin, Unoma Azuah, Promise Okekwe, Angela Nwosu, Nduka Otiono, Akin Adesokan, Ike Okonta, Nehru Odeh, Ebereonwu, Eddie Ayo Ojo, Carlos Idzia Ahmed, Emman Shehu, Okome Onookome, and Toni Kan. Obviously, you know that this is a ridiculously short list. I cannot possibly mention all the writers who transformed the Lagos-Ibadan axis and the Nsukka axis into the two dominant literary hubs in Africa in those heady days.
The haemorrhage did happen. Afam Akeh, Olu Oguibe, Uche Nduka, Sola Osofisan, Godwin Amatoritsero Ede, Okey Ndibe, Biyi Bandele started the early trickle towards Europe. Drip, drip, drip and a generational exodus towards Euro-America had become the defining feature of the generation by the end of the 1990s. Is it really true that exile and flight are the only legacy of this generation? I think that exile, flight, absence, and Richard Ali's question mark tell less than half the story of this generation. We must remember that majority of the members of a generation that has come to be defined by flight and departure did not in fact leave Nigeria.
Toni Kan has always been here. Remi Raji, Uzor Maxim Uzoatu, Chiedu Ezeanah, Denja Abdullahi, Emman Shehu, David Diai, Henry Akubuiro, Omowumi Segun, Toyin Adewale, Nike Adesuyi, Chux Ohai, Jude Dibia, Ike Oguine, Dulue Mbachu, and many prominent members of the third generation never left. Lola Shoneyin, Ogaga Ifowodo, Chuma Nwokolo, and Victor Ehikhamenor (many don't know that he authored Sordid Rituals, a volume of poetry) are all back. Ebereonwu and Austyn Njoku never left till they left. To the extent that it is possible to speak of a renewal of literary modes imagining a new Nigeria beyond the ruination of military rape; to the extent that it is possible to speak of an aesthetic split with previous generations, one has to credit the third generation with such spectres of literary renaissance. My generation produced the first truly new literature which imagined the Nigerian nation in a new way – marking a rupture with the practices and antecedents of previous generation.
Using mainly the poetry of Emman Shehu, Harry Garuba has described the aesthetic departure of the third generation from the practices of preceding generations in terms of a decentering of the mytho-ritualistic bases from which the first two generations imagined project nationhood. Here, nobody is going to make Ogun or any of the weird characters in A Dance of the Forests the organizing principle of an imagined nationhood; nobody is going to invoke the matricial or nativist essence of mother Idoto as a pathway to personal and national becoming; nobody is going to expect a new Nigerian nation to say yes so that her Chi may echo yes in return.
Even in the context of SAP and military despotism, the evacuation of the mytho-ritualistic centre as the basis of engaging and imagining project nationhood led to textual adventurism and thematic daring on a scale previously absent from the Nigerian imagination. Nigeria could now be imagined as a postmodern force-field of play in which other emotions, other psychologies, other realities beyond the admonitions of history and culture could be summoned to feed the psychic will and desire of the patriotic self for anchorage in a hostile national space. Consider the difference in the use of laughter as a motif in the poetry of Niyi Osundare and Remi Raji. Osundare's laughter is bitter, the sort of ironic laughter which is said to be worse than crying in Yoruba lore. The reality of the homeland that Osundare is engaging calls precisely for such a deployment of laughter. Laughter in Remi Raji's poetry does not serve the purpose of lamentation. It is indicative of the poet's ability to find spaces of love for a scorched and scorching homeland.
But Remi Raji is not alone. Freedom from the mytho-ritualistic imperative is what accounts for one of the most powerful enactments of patriotic attachment to fatherland in Nigerian poetry. Who could have thought that this could happen outside of a cultural-nationalist praxis involving the salute of a mytho-ritualistic source? But Olu Oguibe pulled it off. "I am bound to this land by blood" announces the poet persona of his great poem of the same title with considerable gusto. And with this anthem-poem – arguably the most famous poem of my generation – inaugurated what you could call a poetics of love as the predominant ritual of relating to fatherland in Nigerian letters. In previous traditions, love took the indirect route of admonition, reproach, and chastisement for the errors of the rendering; chastisement for the roads not taken. With my generation, a poetics of boundless love was unleashed.
In fact, if you take a look at their collections in the 1990s, there is hardly any that doesn't contain a section of love poems. We know, since Pablo Neruda and Leopold Sedar Senghor, that love poems addressed to named or unnamed women aren't always what they seem to be on the surface. We know that the body of the named woman or unnamed woman is often a double entendre unto which is grafted an idea of the fatherland, Chile in the case of Neruda, Senegal in the case of Senghor. Study the love poems of Ogaga Ifowodo, Remi Raji, Nduka Otiono, Toyin Adewale, Unoma Azuah, and you will see Nigeria the fatherland grinning at you beyond the silhouette of the male or female partner who is the ostensible addressee of each poem. Those who still pay attention to the poetry of the 1990s at all – remember I told you that they have been silenced as a corpus; Lola Shoneyin is remembered today as the author of Baba Segi, nobody remembers So All the Time I was Sitting on an Egg; most people will tell you the title of Unoma Azuah's novels, few will remember the title of her poetry collection – focus all their critical energies on the naked denunciation of military rule.
There is one more thing that Nigeria did in terms of imagining Nigeria which I believe has come to characterize the textualities and identity politics of the young writers I believe have been referred to as post-centenary writers by the conveners of this conference. I have obviously taken the liberty to expand the brief of our discussion so that we do not reduce newness to any generation. Every generation had ways of imagining Nigerian newness, of imagining Nigeria newly. My generation screamed that Nigeria can be loved beyond reproach because we a rebound to her by blood. But we also screamed that love for her must never imply limiting our creative energies and imagination to chronicling and narrativizing her. We found ways to extend her into the world in a postmodern actuation of the transnational imperative. believe have been referred to as post-centenary writers by
Harry Garuba opines that the extension of Nigeria as a self into the self of the global and the transnational begins in the poetry of Emman Shehu. I think a similar claim can be made for Uche Nduka. After the chronicle of the life of my generation that he offered in the cinematic clip strategy ofChiaroscuro, it is safe to say that Uche went on to embrace the world, fashioning a poetics unmoored in immediately localizable national anchors. The embrace of the world. The transnational imagination. The Afropolitan persona. If you move beyond what Uche Nduka has been writing and publishing after Chiaroscuro – consider his poem, "Aquacade in Amsterdam"; if you move beyond the Toronto peregrinations of the poet persona in Amatoritsero Ede's "Globetrotter", if you move beyond the imaginative transnational crossings of Chris Abani's Elvis in Graceland, if you move beyond Sarah Ladipo Manyika's attempt to reproduce the London errantry of earlier generations in In Dependence, you encounter a new generation that must grapple with the identity politics of Afropolitanism and the attendant contradictions of trying to imagine a new Nigeria in an existential context which daily reminds them that the world is now their playground.
Not so long ago, when I was cutting my poetic teeth and doing the usual runs in the writing confraternities of Ibadan and Lagos, I had to rely on the good offices of the British Council or the Alliance Française to encounter the world infrequently. I had a Nigeria that could be symbolically sealed off and made exclusively amenable to my imagination and that of my contemporaries. This is not possible for Richard Ali, Tolu Ogunlesi, Paul Liam, Olisakwe Ukamaka Evelyn, Abubakar Adam Ibrahim, Okwui Obu, A. Igoni Barrett, Ifedigbo Nze Sylva, Jumoke Verissimo, Chinyere Obi-Obasi, Egbosa Imasuen, Uche Peter Umez, Su'eddie Vershima Agema, Onyekachi Peter Onuoha, Rosemary Ede, Saddiq M. Dzukogi and so many other members of the new generation now animating and rocking the Nigerian literary scene.
If a hermetic national locale is not available to them, it is not because they must contend with the deracination of some of their contemporaries who have found their way into Nigeria's literary consciousness while being basically of the global north – Helen Oyeyemi, Tope Folarin, Chibundu Onuzor, etc; it is not because any of them is carried away by the delusions of Teju Cole and Taiye Selasie whose idea of cosmopolitanism is a disavowal of the tag, African or Nigerian writer; it is not because they cannot write creative works set in Nigerian realities – kaine Agary's Yellow Yellow, Richard Ali's City of Memories, Sylva Ifedigbo's The Funeral Did Not End, Isaac Attah Ogezi's Under a Darkling Sky, it is mainly because new modes of transnational alterity prevent exclusive claims to national conversations.
The impossibility of national exclusiveness is played out daily on social media. This is not just the generation of flash fiction and what I have called twitterature elsewhere, they are a generation whose Nigerian literary conversations must engage the "Nigerianness" of Binyavanga Wainaina, Barbara Mhangami, NoViolet Bulawayo, and Christie Watson. Wole Soyinka and co had for raw material a Nigeria that was brutalized by colonialism and handed over to them; Femi Osofisan and co. had for raw material in which the lofty dreams of independence had collapsed and they went in search of utopia wielding fiery Marxism; my generation had for raw material a Nigeria sapped beyond recognition by SAP and military rape and we tried out a poetics of love on her; Pa Ikhide's children have for raw material a borderless Nigeria whose literary axes are located not just in Ake and Port Harcourt but also in Kenya, Uganda, and other funny places from which we read the tweets and Facebook their Facebook updates.
This new reality gives them an advantage though. Never mind the complexities and fault lines of Nigeria that we love to retail, all preceding generations had a rough national referent that we could call the single story that is Nigeria, for want of a better expression. Today, the story has been atomised and scattered all over the place like broken China in the sun. That is why Chimamanda writes Half of a Yellow Sun and we encounter subtle counter stories in Musdoki and City of Memories. Those looking at Nigeria and Africa from the outside may still threaten her with the danger of a single story. The new writers imagining her from within do not run such risks. They have at their disposal not a single story or referent but multiple and fragmented stories. The dilemma we face with a generation in possession of multiple narratives of a single nation in the age of social media is this: will their stories become a thousand flowers armed with the inalienable right to bloom under the sun or will their stories be Babel?
Nigerian Literary Babel. Courtesy: Su'eddie Vershima Agema and Servio Gbadamosi
Being keynote speech presented by Professor Pius Adesanmi on November 13 at the 2014 MBA International Literary Colloquium in Minna, Niger State.
Posted in ESSAYS AND LITERARY JOURNEYS, EVENTS, LITERARY MISSIONARY
LITERARY EVENTS IN NOVEMBER 2014… Su'eddie Vershima Agema
4th EDITION OF MBA COLLOQUIUM TO HOLD IN MINNA 10th-12th November, 2014
Pius Adesanmi to give keynote…
The 4th edition of the Muazu Babangida Aliyu (MBA) International Literary Colloquium comes up from 10th – 12th November, 2014. This year's Colloquium will run for two days; Pre-colloquium day (for commissioning of Multimedia and Arts Gallery) and the Colloquium proper which will have in attendance notable writers from home and abroad, literary critics, social activists, politicians, students and academics across the nation to grace the event. This year's theme is Literature Culture and Social Re-engineering for National Development. Scholars, writers, creative performers and literary journalists from across the country would grace the occasion.
2014 colloquium will feature a pre-colloquium lecture with the theme: Post Centenary Nigeria: New Literatures, New Leaders, New Nation to be presented by Professor Pius Adesanmi of the University of Carleton, Canada. There will also be an Interactive session on Literature, Creative writing, Leadership and Democracy among students, writers and politicians. It looks like the long awaited first books of the Association of Nigerian Authors (ANA) Nigerian Writers' Series books would be unveiled there…
Nigerian writers remain grateful to Prof. Mohammed Kuta Yahaya who facilitated all of the above.
Venue would be Justice Idris Legbo Kutigi Hall on 11th and 12th, 11am and 10am respectively for the lectures… UK Bello Arts Theatre would host the writers on 10th and 11th, 7pm…
LAGOS BOOK AND ARTS 'FREEDOM' FESTIVAL IN HONOUR OF SOYINKA TO HOLD NOVEMBER 14th – 16th
-Biodun Jeyifo, Chuma Nwokolo, Toni Kan, Rotimi Babatunde, Kola Tuboson, Sage Hasson to feature prominently…
The 16th Lagos Book and Arts Festival (LABAF) is set to hold on the 14th to 16th November, 2014. LABAF this year is in honour of Wole Soyinka at 80. The three day event would feature a variety of events that writers and readers would find desirable and memorable. The theme for this year's festival is 'Freedom and the Word'. The official website for the festival explains that this theme was inspired by the 25th anniversary of the fall of the Berlin Wall, 20th Anniversary of the South African Democracy and 15th anniversary of Nigeria's 4th Republic. The Festival largely interpretes how the published text has interrogated the notion of human freedom.
The event would include addresses by Professor Biodun Jeyifo and Prof. Chidi A. Odinkalu in honour and exploration of Soyinka—the man and his books. There would also be book and writing discussions, panel conversations led by distinguished writers like Chuma Nwokolo (editor of African Writing and author of Diaries of a Dead African, The Ghost of Sani Abacha among other titles); Toni Kan, Kola Tuboson, Rotimi Babatunde (Caine Prize Winner 2012), and the spoken word maestro, Sage Hasson among others.
Every year, the Lagos Book and Arts Festival brings together writers from around the year to interact with each other and explore particular theme. It is
The main festival date for this year is 14th to 16th November 2014 and would take place at Freedom Park, Broad Street, Lagos. For more information, please visit http://lagosbookartfestival.org.
AKE FESTIVAL OF ARTS HOLDS 18th – 22nd NOVEMBER
Ake Festival, judged one of the best literary festival for 2013 is back this year to hold 18th to 22nd November 2014
Organised by Lola Shoneyin and the Book Buzz Foundation, the festival would bring writers from around the world in Master Classes where the skills of writers would be honed. The theme for this year is Bridges and Pathways. Discussions this year will focus on building bridges between African peoples, especially along language, ethnic and gender lines, and charting new paths with the aim of creating synergy and cultural cross fertilization on the African continent.
The guest list for the festival this year includes a full who-is-who cast of literary and social giants. Expected guests include Former President of Nigeria, Chief Olusegun Obasanjo; Nobel Laureate Wole Soyinka; President of the Association of Nigerian Authors, Professor Remi Raji for starters. Others include Lizzy Attree, Molara Wood, Mukoma wa Ngugi, Okey Ndibe, Rodney Saint Eloi, Abubakar Adam Ibrahim, Chijioke Amu-Nnadi, Nnedi Okoroafor, Rotimi Babatunde, Toni Kan, E. E. Sule, Eghosa Imasuen, Dami Ajaiyi, Efe Paul Azino, Stella Duffy and Uche Peter Umez, Wale Okediran, Ukamaka Olisakwe among several other distinguished writers, critics, leaders, thinkers, teachers, academics and distinguished personalities from around the world.
The event would include Book Chats, Panel Discussions, Master Classes, Art Exhibition, Drama, Film, Dance, Documentaries, Poetry, School Visits and a tour of Abeokuta.
The Ake festival, in its second year now, is a yearly literary and cultural festival that takes place in Abeokuta. The main official venue for this year is the June 12 Cultural Centre, Kuto, Abeokuta, Ogun State. More information? Visit http://www.akefestival.org/
ANA INTERNATIONAL CONVENTION POSTPONED INDEFINITELY
The 33rd International Convention of the Association of Nigerian Authors (ANA) which was scheduled to hold in Port Harcourt, Rivers State from 30th October to 2nd November, 2014 has been postponed indefinitely. The reason given for the postponement was given by the President of the Association, Remi Raji, as being the inability of the proposed host state, Rivers, to meet deadlines on preparations. The National Executive has apologized for this postponement and promised that a new date, and possibly, a new venue would be announced in due course. Meanwhile, there are indications that the convention would be held in the first week of December somewhere in the Niger-Delta…
More information when that comes up.
So, have a great November people and wherever it is the literary muse takes you…Bon voyage.
Posted in ESSAYS AND LITERARY JOURNEYS, LITERARY MISSIONARY
Interesting… it makes more sense as you read along. Wait, was I mentioned somewhere here too? 🙂
Pa Ikhide
By Professor Pius Adesanmi
Winner, the Penguin Prize for African Writing
Author of You're Not a Country, Africa!
Keynote lecture delivered at the National Convention of the Association of Nigerian Authors
Uyo, Akwa Ibom State
Protocols!
When I first received the theme of this conference in a somber email from the soon-to-be-Dean, Faculty of Arts, University of Ibadan, I wondered what writerly demons took possession of my great friend, Professor Remi Raji, Richard Ali, Denja Abdulahi, D.M. Dzukogi, and other members of the National Executive of the Association of Nigerian Authors, and made them settle on a theme advertising such apparently incompatible terms as literature and security in the same sentence. Being a very active member of literary Cyberia (my neologistic contraction of Cyber and Nigeria), I could understand and relate to the social media part of the theme but security? National security? Was it the demons…


text:
Josh Mcdaniels Biography, Age, Wife, Family, Net Worth and Salary
Josh Mcdaniels born Joshua Thomas McDaniels, is an American football coach. He was born on April 22, 1976. A native of Canton, Ohio, McDaniels is the son of one of the most well-respected high school coaches in the country. Thom McDaniels was a finalist for the NFL High School Coach of the Year award in 2012, and Josh benefitted from growing up in a football family.
BY Admin October 6, 2017
Josh Mcdaniels Biography
McDaniels attended John Carroll University, where he played quarterback and wide receiver for the Blue Streaks from 1995-98. McDaniels was a college teammate of former NFL linebacker London Fletcher, Patriots director of player personnel Nick Caserio, assistant quarterbacks coach Jerry Schuplinski and director of pro personnel Dave Ziegler.
He is the offensive coordinator of the New England Patriots of the National Football League (NFL). Josh McDaniels was named New England Patriots offensive coordinator/quarterbacks coach for the 2012 season after he re-joined the Patriots coaching staff in January 2012 as an offensive assistant.
As offensive coordinator, McDaniels has guided the offense to six top-10 rankings, including the NFL's No. 1 ranked offense in 2007 and 2012. Additionally, Tom Brady has been elected to the Pro Bowl six times and eclipsed 4,000 yards passing for the eighth time in 2016. In 2016 under McDaniels' and assistant quarterbacks coach Jerry Schuplinski's guidance, Patriots quarterbacks set NFL records by going 258 pass attempts to start the season without an interception, throwing just two interceptions overall and had the best touchdown to interception ratio (16.00) in a single season. McDaniels also earned his fifth Super Bowl ring as a coach in 2016, his second as the Patriots offensive coordinator.
McDaniels left the Patriots to become the head coach of Denver in 2009 and in 2011, McDaniels moved on to St. Louis to become the offensive coordinator for the St. Louis Rams. Under McDaniels' guidance that season, Rams RB Steven Jackson eclipsed the 1,000-yard mark for the seventh consecutive season despite running behind an offensive line that started eight different combinations over the course of the season.
McDaniels spent the 2009-10 seasons as the head coach of the Denver Broncos after serving as an assistant with the Patriots from 2001-08. In his first stint with New England, McDaniels played a key role on a staff that won three Super Bowl titles and four AFC championships.
He first joined the Patriots organization as a personnel assistant on the 2001 team that defeated the Rams in Super Bowl XXXVI, served as a defensive assistant on the 2003 club that won Super Bowl XXXVIII and was promoted to quarterbacks coach in 2004, a season that culminated with a victory over the Eagles in Super Bowl XXXIX.
In addition to his role with Patriots quarterbacks, McDaniels added the title of offensive coordinator in 2006. In his second season as New England's offensive play caller, McDaniels oversaw a unit that broke several NFL records, including points scored, touchdowns scored and most players scoring a touchdown.
Under McDaniels' tutelage, 2007 NFL MVP Tom Brady broke the NFL record for touchdown passes in a season (50) and led the league in both passing yards (4,806) and passer rating (117.2). Brady also set franchise marks in nearly every major passing category, while WR Wes Welker set the franchise record for receptions in a season (112) and WR Randy Moss broke the NFL record for touchdown receptions with 23. The Patriots offense also posted the franchise's highest average yards per rush in 22 seasons with 4.1 yards per carry, and the team's 17 rushing touchdowns were the second highest total in the previous 20 years.
During McDaniels' first season in Denver, QB Kyle Orton set career highs for pass attempts, completions, passing yards, touchdown passes and passer rating while tying his career low for interceptions. Orton's 3,802 yards were the sixth highest for a season in Broncos history. In 2010, the Broncos ranked seventh in the NFL in passing yards per game and finished 13th in total yards per game. WR Brandon Lloyd led the NFL in receiving yards with 1,448 and finished second in the AFC with 11 touchdown catches en route to his first Pro Bowl invitation.
Entering 2017, Brady has been elected to eight Pro Bowls during his nine seasons with McDaniels serving as his position coach. After Brady suffered a season-ending knee injury in the 2008 regular-season opener, another McDaniels protégé, Matt Cassel, led the club to a 10-5 record as a starter. In his first season as an NFL starter, Cassel posted the fourth-best completion percentage in Patriots history (63.4) while passing for 3,693 yards with 21 touchdowns and 11 interceptions.
Prior to joining Bill Belichick's staff, McDaniels spent one season as a graduate assistant under Nick Saban at Michigan State.
Josh Mcdaniels Age
He was born on April 22, 1976.
Josh Mcdaniels Wife – Josh Mcdaniels Family
Josh's wife is laura mcdaniels. They have four children.
Josh Mcdaniels Net Worth
He has an estimated net worth of $6 million.
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redpajama_set_name: RedPajamaCommonCrawl


Now the difference, the chickens in my basement were recently hatched baby chicks in a box. Their chickens were full size and of prize worthy stock. And there were dozens of other critters too, some rare and possibly protected.
The listing appointment went well until they said I was the only one who could show their property. I explained there are 15,000 other agents and one of them might have the best buyer. They were visibly concerned by the prospect of getting their property ready for the open market.
I shared that I was showing buyers a couple hobby farms this coming weekend and maybe those buyers would like theirs. To my surprise, the buyers, who had been looking for years for a rambler on acreage, fell in love with this split entry.


Address : Post Master, Erkudi Achampathu, Madurai South Taluk, Madurai District, TAMIL NADU, India.
What is Pin code of Erkudi Achampathu?
Erkudi Achampathu Pin code or Postal zip code of Erkudi Achampathu is an unique Post index number provided to the post office of Erkudi Achampathu, Madurai Division, Madurai District of Tamil Nadu state, India. These Pin codes are provided by India Post.


text:
TIMITHY BRADLEY STOPS BRANDON RIOS
Timothy Bradley stops Brandon Rios, an odd sight
By Tim Starks - yardbarker.com
Boxing watchers tend to see too much of one side or the other when a fighter looks good and his opponent looks bad. Both sides were in exhibition in roughly even — and extreme — measure Saturday on HBO when a nimble, disciplined Timothy Bradley, newly under the tutelage of Teddy Atlas, surprisingly stopped the previously unstopped Brandon Rios, who was so out of it he retired in the ring afterward.
Steve Marcus/Getty Images
Basically, Bradley looked the best he's looked in a long, long time, like Atlas had lit a spark under him both emotionally and intellectually in the ring. Rios, who struggled to make weight, talked beforehand about losing his fire and fought like it. In theory, Rios was likely to make Bradley look good even if he was motivated, given that Bradley is an athletic boxer and Rios is a slow slugger. But given Bradley's tendency to get wobbled, given his tendency to get sucked into a brawl, and given Rios' tendency to overachieve, it had a chance on paper of being an excellent fight.
None of those factors came into play. Bradley in the 2nd round did briefly get sucked into an exchange up close with Rios, a round Rios arguably won. Most of the rest of the time, Bradley was in full command. He moved more than usual. He defended better. Atlas emphasized concentration every round, emphasized beating Rios to the punch, emphasized stepping around and firing. Bradley commanded every round other than the 2nd by absorbing those lessons.
Rios, meanwhile, started slow, heated up a little in the 2nd round, and by the 5th appeared out of gas already. He couldn't find Bradley and didn't seem even to want to. He ballooned up to 170 for fight night, and Bradley made that soft body pay. It was a combo of body shots that dropped Rios in the 9th, and he struggled to rise before 10. Another series of body shots dropped him again, and the ref waived it off. It was odd to see any "give" in Rios because he's been hit by the best and stayed where he was — right in his opponent's face. But in a post-fight interview, he complained of a long layoff, said he'd had some long wars and "My body's not the same no more… I think it's time to hang it up. I'm done."
If it's the end for Rios, nobody should be surprised. His was a fighting style that wasn't meant to last very long — to burn brightly, to make memorable wars, and then snuff out in short order. That looks exactly like what has happened.
On the other side, 31-year-old Timothy Bradley is on course for a late-career resurgence. Atlas' rhetorical excess isn't for everybody, nor are his unhinged rants. They actually seem to motivate Bradley — the sequence where Atlas was shouting things that were like parodies of Atas ("We're firemen! Flames don't intimidate us!") was followed by Bradley going out even harder in the very next round. And Atlas haters can act like the man doesn't see the sport well, but he definitely does. He's had fighters who tuned him out, but in Bradley he might have the ideal boxer with whom he can match, as HBO's Max Kellerman pointed out.
But let's not get carried away, as HBO's Jim Lampley seemingly did afterward, by suggesting Bradley could be competitive with Canelo Alvarez at 154. Bradley at welterweight is stretching how high he can compete already.
Speaking of getting carried away: Sheesh, you might've thought on the undercard that Lampley, Kellerman and Roy Jones were about to fight to the death to see who could get the honor, mid-fight, of giving a hummer to Vasyl Lomachenko. Look, he's a nice fighter, everybody knows it. But the featherweight wasted everybody's time but his with his Foregone Conclusion — oops, I mean Win — against Romulo Koasicha. At one point Kellerman tried to convince us that Koasicha was "regular world class" and Lomachenko was "special world class," as if those distinctions made any sense verbally let alone could even be used to describe Koasicha. He's not world class in any way. Show me on his record anything he did to earn that designation. All the while, the broadcast team was comparing Lomachenko to every great, past and present, who graced the ring. And naturally they talked him up as a guy who might be pound-for-pound #1, when, at best, the guy is sniffing the top 20. And naturally all the other guys they listed as p4p #1 contenders were HBO fighters.
Get Lomachenko in against a viable opponent, or let's move him off TV. He's taken three nothing fights in a row. We can make a thing about the lack of opponents at featherweight who aren't affiliated with rival promoters, but glance at the division and those around it and tell me you can't find people much, much better than he's been facing since that hot start of his.
Source: www.yardbarker.com/boxing/articles/timothy_bradley_stops_brandon_rios_an_odd_sight/s1_10150_19850242


Our next big open day event is here! We are going to be at Rookery Meadow with all 3 of our styles of marquee ready for a weekend of fun! Read on for more information.
Two-day event this September celebrates marriage whilst supporting those who have decided to split.
Morton's Event Hire and Rookery Meadow have teamed up for a two-day event on 29thand 30thSeptember showcasing some of East Anglia's most bespoke venue and wedding suppliers.
Both from farming routes, the two family-owned events companies now help people create the perfect place to celebrate any occasion. This year they have joined forces for a wedding extravaganza like no other.
Morton's Event Hire will be setting up all three of their fabulous marquees, a stunning traditional pole and line marquee, the totally unique 'stretch' tent, and their ever-versatile modular pods. Each will be decorated along a different theme to reflect the varying vibes that a marquee wedding can offer.
Suppliers from across East Anglia, including caterers, mobile bars, florists, musicians and venue stylists, will showcase their services at the family friendly, free to enter event.
Rather than charge suppliers to exhibit at the open day, the event organisers are asking exhibitors to donate to a very worthy local charity, Norfolk Family Mediation Service.
The event will run on 29th and 30th September at Rookery Meadow, Rookery Farm, St Margaret South Elmham, Harleston, IP20 0PN from 11am to 4pm each day. Entry is free.


text:
\section{Introduction}

With the rapid development of digital cameras and proliferation of
social media sharing, there is also an explosive growth of available
figure skating sports videos in both the quantity and granularity.
Every year there are over 20 international figure skating competitions
held by International Skating Union (ISU) and hundreds of skaters
participated in them. Most of the high-level international competitions,
such as ISU championships and ISU Grand Prix of Figure Skating are
broadcast on the worldwide broadcaster, for instance CBC, NHK, Eurosport,
CCTV. Over 100 figure skating videos are uploaded in Youtube and Dailymotion
a day during the season.

The analysis of figure skating sports videos also have many real-world
applications, such as automatically scoring the players, highlighting
shot generation, and video summarization. By the virtue of the state-of-the-art
deep architectures and action recognition approaches, the techniques
of analyzing figure skating sports videos will also facilitate statistically
comparing the players and teams, analyzing player's fitness, weaknesses
and strengths assessment. In terms of these sport statistics, professional
advice can be drawn and thus help the training of players.

Sports video analytics and action recognition in general have been
extensively studied in previous works. There exist many video datasets,
such as Sports-1M \cite{deepvideo}, UCF 101\cite{Soomro2012}, HMDB51
\cite{HMDB51}, FCVID \cite{fcvid_2017} and ActivityNet \cite{activitynet}.
These datasets crawled the videos from the search engines (\emph{e.g.},
Google, or Bing) or the social media platforms (e.g., YouTube, Flickr,
\emph{etc}). The videos are crowdsourcingly annotated. On these video
datasets, the most common efforts are mainly made on video classification
\cite{jiang2011consumervideo,deepvideo}, video event detection \cite{over2011trecvid},
action detection and so on.

Remarkably, inspired by Pirsiavash \emph{et al}. \cite{quality_action},
this paper addresses a novel task of learning to score figure skating
sport videos, which is very different from previous action recognition
task. Specifically, in this task, the model must understand every
clip of figure skating video (\emph{e.g.}, averagely 4400 frames in
our Fis-V dataset) to predict the scores. In contrast, one can easily
judge the action label from the parts of videos in action recognition.
For example, one small video clip of capturing the mistake action
of the player, will significantly negatively affect the final scores
in our task. Thus the model must fully understand the whole video
frames and process the varying length of videos.

Quite a few works have been devoted to learning to score figure skating
videos.  The key challenges come from several aspects. First, different
from consumer videos, figure skating videos are the professional sports
videos with the longer length (averagely 2 minutes and 50 seconds).
Second, the scores of figure skating videos should be contributed
by the experts or referees; in contrast, the labels of previous classification/detection
based video analysis tasks are collected in a crowdsourcing way. Third,
not all video segments can be useful to regress the scores, since
the referees only take account into scores those clips of technical
movements (TES) or a good interpretation of music (PCS).

\begin{figure*}
\begin{centering}
\includegraphics[scale=0.3]{overview}
\par\end{centering}
\caption{\emph{\label{fig:Example-frames-of}}Example frames of our figure
skating video dataset. Each row is corresponding to one video.}
\end{figure*}

To address these challenges, we propose an end-to-end framework to
efficiently learn to predict the scores of figure skating videos.
In particular, our models can be divided into two complementary subnetworks,
\emph{i.e.}, Self-Attentive LSTM (S-LSTM) and Multi-scale Convolutional
Skip LSTM (M-LSTM). The S-LSTM employs a simple self-attentive strategy
to select important clip features which are directly used for regression
tasks. Thus the S-LSTM mainly learns to represent the local information.
On the other hand, the M-LSTM models the local and global sequential
information at multi-scale. In M-LSTM, we utilize the skip LSTM to
efficiently save the total computational cost. Both two subnetworks
can be directly used as the models for prediction, or integrated into
a single framework for the final regression tasks. Our models are
evaluated on two figure skating datasets, \emph{namely}, MIT-skate
\cite{quality_action} and our own Fis-V video dataset. The experiments
results validate the effectiveness of our models.

To further facilitate the research of learning to score figure skating
videos, we contribute the Figure Skating Video (Fis-V) dataset to
the community. The Fis-V dataset has the videos of high quality as
well as the scores labeled. Specifically, our videos in Fis-V are
captured by professional camera devices. The high standard international
figure skating competition videos are employed as the data source
to construct Fis-V dataset. The example video frames of this dataset
are shown in Fig. \ref{fig:Example-frames-of}. Each video snapshots
the whole performance of one skater only; the irrelevant parts towards
the skater (such as warming up, bowing to the audience after the performance)
are pruned. Thus the length of each video is about 2 minutes and 50
seconds. Totally, we collect 500 videos of 149 professional figure
skating players from more than 20 different countries. We also gather
the scores given by nine different international referees in the competitions.

\vspace{0.1in}

\noindent \textbf{Contributions. }We highlight the three contributions.
(1) The proposed Self-Attentive LSTM can efficiently learn to model
the local sequential information by a self-attentive strategy. (2)
We propose a Multi-scale Convolutional Skip LSTM model in learning
the local and global information at multi-scale, while it can save
the computational cost by skipping some video features. (3) We contribute
a high quality figure skating video dataset \textendash{} Fis-V dataset.
This dataset is more than 3 times bigger than the existing MIT-skate
dataset. We hope this dataset can boost the research of learning to
score professional sports videos.

The rest of this paper is organized in such a way. Sec. \ref{sec:Related-Work}
compares some related work. We describe the details of constructing
the dataset in Sec. \ref{sec:Figure-skating-Video}. The methodology
of solving the specific task is discussed in Sec. \ref{sec:Methodology}.
We finally give the experimental results in Sec. \ref{sec:Experiments}.
The whole paper is concluded in Sec. \ref{sec:Conclusion}.

\section{Related Work\label{sec:Related-Work}}

The sheer volume of video data makes the automatic video content understanding
difficulty intrinsically. Very recent, deep architectures have been
utilized to extract feature representations effectively in the video
domain. While the development of image representation techniques
has matured quickly in recent years \cite{gupta_pami2009,graph_matching,human_still_latent,distributed_repre_cvpr2011,person_interaction},
more advanced architectures were conducted for video understanding
\cite{retrieving_movie,action_bank,efros2003action}, including Convolutional
Networks (ConvNets) with Long Short-Term Memory (LSTMs) \cite{donahue_cvpr2015,snippet_cvpr2015}
and 3D Convolutional Networks \cite{two_strems_nips} for visual recognition
and action classification, two-stream network fusion for video action
recognition \cite{snippet_cvpr2015,two_strems_nips}, Convolutional
Networks learning spatiotemporal features \cite{taylor_eccv2010,Tran_ICCV2015}.
We discuss these previous works in each subsection.

\subsection{Video Representation}

Previous research on improving video representations focuses on local
motion features such as HOF \cite{laptev2008actions}{[}24{]} and
MBH \cite{MBH} in the Dense Trajectories feature \cite{dense_traj}
and the corresponding variants \cite{improved_dense}. The success
of deep learning in video analysis tasks stems from its ability to
derive discriminative spatial-temporal feature representations directly
from raw data tailored for a specific task ~\cite{Ji2010,C3D}.

Directly extending the 2D image-based filters \cite{KrizhevskySH12}
to 3D spatial-temporal convolutions may be problematic. Such spatial-temporal
Convolutional Neural Networks (CNN), if not learned on large-scale
training data, can not beat the hand-crafted features. Wang \emph{et
al}. \cite{Wang2013a} showed that the performance of 3D convolutions
is worse than that of state-of-the-art hand-crafted features. Even
worse, 3D convolutions are also computationally expensive; and it
normally requires more iterations to train the deep architectures
with 3D convolutions than those without.

To reduce such computational burden, Sun \emph{et al}. proposed to
factorize spatial-temporal convolutions ~\cite{sun2015human}. It
is worth noting that videos can be naturally considered as an ensemble
of spatial and temporal components. Motivated by this observation,
Simonyan and Zisserman introduced a two-stream framework, which learn
the spatial and temporal feature representations concurrently with
two convolutional networks~\cite{Simonyan2014}.  Such a two stream
approach achieved the state-of-the-art performance on many benchmarks.
Furthermore, several important variants of fusing two streams are
proposed, such as \cite{cvpr15:wang,Feichtenhofer16,WangXWQLTV16,ZhangWWQW16,Wang_Transformation,zhu2016key,bilen2016dynamic,icmr15:eval2stream}

Most recently, C3D \cite{C3D}, and SENet \cite{SEnet} have been
proposed for powerful classification models on videos and images.
C3D \cite{C3D} utilized the $3\times3\times3$ spatial-temporal convolution
kernels and stacked them into a deep network to achieve a compact
representation of videos. C3D has been taken as a more effective structure
of preserving temporal information than 2D CNNs. SENet \cite{SEnet}
adopted the ``Squeeze-and-Excitation'' block, which integrates the
channel-set features, stressing the independencies between channels.
In this work, we employ the C3D as the basic video feature representation.

\subsection{Video Fusion}

In video categorization systems, two types of feature fusion strategies
are widely used, \emph{i.e.}, the early fusion and the late fusion.
Multiple kernel learning \cite{mkl_2004} was utilized to estimate
fusion weights \cite{heterog_iccv_2009,natarajan_2012}, which are
needed in both early fusion and late fusion. To efficiently exploit
the relationships of features, several more advanced feature fusion
techniques were conducted. An optimization framework in \cite{late_fusion_2012}
applied a shared low-rank matrix to reduce noises in the fusion. An
audio-visual joint codebook proposed by Jiang\emph{ el al.} \cite{jiang_acmmm2009}
discovered and fused the correlations of audio and visual features
for video classification. The dynamic fusion is utilized in \cite{liu_2013_cvpr}
as the best feature combination strategy.

With the rapid growth of deep neural networks, the combination of
multiple futures in neural networks gradually comes into sight. In
multimodal deep learning, a deep de-noised auto-encoder \cite{multimodal_icml2011}
and Boltzmann machines \cite{Srivastava_NIPS2012} were employed to
fuse the features of different modalities. More recently, Recurrent
Neural Networks have also been utilized to fuse the video representation.
\textcolor{red}{}Wu \emph{et al}. \cite{wu2016multi} modeled videos
into three streams including frames, optical flow and audio spectrogram
and fuse classification scores adaptively from different streams with
learned weights. Ng \emph{et al}. \cite{pyarmid_video_fusion} employed
time domain convolution or LSTM to handle video structure and use
late fusion after the two-stream aggregation. Comparing with this
work, we propose a fusion network to efficiently fuse the local and
global sequential information learned by the self-attentive and M-LSTM
models.

\subsection{Sports Video Analysis}

Recently, the sports video analysis has been tropical in the research
communities \cite{camera_light}.  A common and important unit in
sports video analysis is the action, or a short sequence of actions.
There are various works that assess how well the people perform actions
in different sports, including an application of automated video assessment
demonstrated by a computer system that analyzes video recordings of
gymnasts performing the vault \cite{gordon_1995}; a probabilistic
model of a basketball team playing based on trajectories of all players
\cite{jug_2003}; the trajectory-based evaluation of multi-player
basketball activity using Bayesian network \cite{perse_2007}; and
machine learning classifier on top of a rule-based algorithm to recognize
on-ball screens \cite{McQueen2014}.

The tasks of learning to score the sports have less been studied with
only two exceptions \cite{quality_action,parmar2017learning}. Pirsiavash
\emph{et al}. \cite{quality_action} introduced a learning-based framework
evaluating on two distinct types of actions (diving and figure skating)
by training a regression model from spatiotemporal pose features to
scores obtained from expert judges. Parmar \emph{et al.} \cite{parmar2017learning}
applied Support Vector Regression (SVR) and Long Short-Term Memory
(LSTM) on C3D features of videos to obtain scores on the same dataset.
In both \cite{quality_action,parmar2017learning}, the regression
model is learned from the features of video clips/actions to the sport
scores. Comparing with \cite{quality_action,parmar2017learning},
our model is capable of modeling the nature of figure skating. In
particular, our model learns to model both the local and global sequential
information which is essential in modeling the TES and PCS. Furthermore,
our self-attentive and M-LSTM model can alleviate the problem that
figure skating videos are too long for an ordinary LSTM to get processed.

\section{Figure Skating Video (Fis-V) Dataset\label{sec:Figure-skating-Video}}

Our figure skating video dataset is designed to study the problem
of analyzing figure skating videos, including learning to predict
scores of each player, or highlighting shots generation. This dataset
would be released to the community under necessary license.

\subsection{Dataset construction}

\begin{figure*}
\begin{centering}
\includegraphics[scale=0.4]{match}
\par\end{centering}
\caption{\label{fig:matches}Results of the correlations over different matches.
The correlation values are visualized in Y-axis; and the X-axis denotes
different matches. For instance, ``13COC'' means the COC (Cup of
China) held in 2013.}
\end{figure*}

\noindent \textbf{Data source}. To construct the dataset, we search
and download a great quantity of figure skating videos. The figure
skating videos come from formal high standard international skating
competitions, including NHK Trophy (NHK), Trophee Eric Bompard (TEB),
Cup of China (COC), Four Continents Figure Skating Championships (4CC)
and so on. The videos of our figure skating video dataset are only
about the playing process in the competitions. Note that the videos
about figure skating may also be included in some previous datasets
(\emph{e.g.}, UCF 101\cite{Soomro2012}, HMDB51 \cite{HMDB51}, Sports-1M
\cite{deepvideo} and ActivityNet \cite{activitynet}), which are
constructed by searching and downloaded from various search engines
(e.g., Google, Flickr and Bing, etc), or the social media sharing
platforms (e.g. Youtube, DailyMotion, etc.). Thus the data sources
of those datasets are different from ours. We thus emphasize the better
and more consistent visual quality of our TV videos from the high
standard international competitions than those consumer videos downloaded
from the Internet. Additionally, the consumer videos about figure
skating may also include the practice videos.

\vspace{0.1in}

\noindent \textbf{Selection Criteria.} We carefully select the figure
skating videos used in the dataset. We assume the criterion of scores
of figure skating should be consistent for the high standard international
skating competitions. Thus to maintain standard and authorized scoring,
we select the videos only from the highest level of international
competitions with fair and reasonable judgement. In particular, we
are using the videos from ISU Championships, ISU Grand Prix of Figure
Skating and Winter Olympic Games. Totally we have the videos about
149 players from more than 20 different countries. Furthermore, in
figure skating competitions, the mark scheme is slightly changing
every season, and very different for men and women. To make the scores
more comparable, only the competition videos about ladies' singles
short program happened over the past ten years are utilized in our
figure skating video dataset. We also collect the ground-truth scores
given by nine different referees shown in each competition.

\vspace{0.1in}

\noindent \textbf{Not rush videos}. The rush videos often refer to
those unedited videos, which normally contain redundant and repetitive
contents. The videos about figure skating in previous datasets may
include those unedited and ``rush'' parts about the players, such
as warming up, bowing to the audience after the performance, and waiting
for scores at the Kiss\&Cry. These parts may be not necessarily useful
to help judge the scores of the performance of figure skating. In
contrast, we aim at learning the model of predicting the scores purely
from the competition performance of each player, rather than from
the ``rush'' parts. Thus those unedited parts are pruned in our
videos. More interestingly and importantly, in the sports videos of
multiple players, the videos have to track, locate and transit different
players. Our figure skating video has about only one player, and the
whole video is only tracking, and locating the player over her whole
performance as shown in Fig. \ref{fig:Example-frames-of}.

\begin{figure*}
\begin{centering}
\includegraphics[scale=0.4]{player}
\par\end{centering}
\caption{\label{fig:players}Results of the correlations of different players.
The Y-axis indicates the computed correlation values; and the X-axis
annotates the name of each skater. }
\end{figure*}

\subsection{Pre-processing and Scoring }

\vspace{0.1in}

\noindent \textbf{Pre-processing}. We initially downloaded 100 hour
videos; and the processing procedure is thus needed to prune some
low quality videos. In particular, we manually select and remove the
videos that are not fluent nor coherent. To make sure the figure skating
videos exactly correspond to the ground-truth scores, we manually
processed each video by further cutting the redundant clips (e.g.
replay shots or player's warming up shots). We only reserve the video
from the exact the beginning of each performance, to the moment of
ending pose, with duration of about 2 minutes and 50 seconds. Particularly,
this time slot also meets the duration of skating stipulated by the
International Skating Union, which is 2 minutes and 40 seconds within
10 seconds plus or minus for ladies\textquoteright{} singles short
program. Each video has about 4300 frames with the frame rate 25.
Thus both the number of frames and videos are far larger than the
dataset released in \cite{quality_action}.

\vspace{0.1in}

\noindent \textbf{Scoring of figure skating}. We carefully annotated
each video with the skater and competition, and labeled it with two
scores, \emph{namely}, Total Element Score (TES) and Total Program
Component Score (PCS). These scores are given by the mark scheme of
figure skating competition. Specifically, these scores measure the
performance of skater at each stage over the whole competition. The
score of TES is used to judge the difficulty and execution of all
technical movement; and PCS aims at evaluating the performance and
interpretation of the music by the skaters. Both the TES and PCS are
given by nine different referees who are the experts on figure skating
competition.  Note that the same skater may receive very different
scores at different competition due to her performance. Finally we
gather 500 videos about ladies' singles short program, and each video
comes with the ground-truth scores. We randomly split the dataset
into 400 training videos and 100 testing ones.

\subsection{Data Analysis}

Apart from learning a score prediction model by using this dataset,
we conduct statistical analysis and have some interesting finding.
In particular, we compute the Spearman correlation and Kendall tau
correlation between TES and PCS over different matches (in Fig. \ref{fig:matches})
or different players (in Fig. \ref{fig:players}). More specific,
we take the TES and PCS values of all skaters in each match, and compute
the correlations as shown in Fig. \ref{fig:matches}. These values
reflect how the TES and PCS are correlated across different matches.
On the other hand, we take the same skater TES and PCs values of all
matches she took, and calculate their correlations in Fig. \ref{fig:players}.

As shown in Fig. \ref{fig:matches}, we find that in over a half of
all matches, the Toal Element Score (TES) has little correlation with
Total Program Component Score (PCS). This is reasonable, since the
TES and PCS are designed to measure two quite different perspectives
of the skater's performance in the whole competition. In other words,
TES and PCS should be relatively independent distributed. In a few
matches, we indeed observe the high correlation between TES and PCS
as in Fig. \ref{fig:matches}. We attribute this high correlation
to the subjectivity of referees, \emph{i.e}., referees would think
that the skaters who can complete difficult technical movements (TES)
are also able to interpret the music well (PCS). Furthermore, the
weak correlations between TES and PCS are also shown in Fig. \ref{fig:players}.

\noindent \textbf{Fis-V dataset Vs. MIT-skate dataset}. Comparing
with the existing MIT-skate dataset \cite{quality_action}, our dataset
has larger data scale (\emph{i.e.}, more than 3 times videos), higher
annotation quality (\emph{i.e.}, For each video, we provide both PCS
and TES scores provided, rather than a single total score), and collecting
more update-to-date figure skating videos (\emph{i.e.,} our videos
come from 12 competitions from 2012 to 2017) than the MIT-skate dataset.
Particularly, all of videos in MIT-Skate are from competitions happened
before 2012, which makes the dataset somehow outdated, since the scoring
standards of figure skating competitions is constantly changing in
the international competitions. We think a qualified figure skating
video dataset should be updated periodically.

\section{Methodology\label{sec:Methodology} }

In this section, we present our framework of learning to score the
figure skating videos. We divide the whole section into three parts.
Sec. \ref{subsec:Problem-Setup} discusses the problem setup and the
video features we are using. We discuss how to get video level representation
in Sec. \ref{subsec:Video-Level-Representation-by}. Finally, the
video fusion scheme of learning to score will be explained in Sec.
\ref{subsec:Video-fusion-by}.

\begin{figure*}
\centering{}\includegraphics[scale=0.33]{model3}\caption{\label{fig:model}Overview of multi-scale convolution aggregation
model with skip-LSTM. }
\end{figure*}

\subsection{Problem Setup\label{subsec:Problem-Setup}}

\vspace{0.1in}

\noindent \textbf{Weakly labeled regression.} In figure skating matches,
the referees will incrementally add the TES with the progress of the
whole competition on-the-fly. Once the player finished one particular
technical movement, the corresponding TES and PCS scores will be added.
Ideally, we want the scores of each technical movement; but in the
real situation, it is impossible to get the incrementally added scores
synchronized with each video clip. Thus, we provide the final scores
of TES and PCS; and the tasks of predicting these scores can be formulated
as weakly labelled regression tasks. In our tasks, we take the prediction
of TES and PCS as two independent regression tasks.

\vspace{0.1in}

\noindent \textbf{Video Features. }We adopt deep spatial-temporal
convolution networks for more powerful video representation. We extract
deep clip-level features off-the-shelf from 3D Convolutional Networks,
which are pre-trained on large-scale dataset. In particular, We use
the 4096 dimensional clip-based feature from the \emph{fc6} layer
of \emph{C3D} \cite{Tran_ICCV2015} pre-trained on Sports-1M \cite{sports1m},
which is a large-scale dataset containing 1,133,158 videos which have
been annotated automatically with 487 sports labels. We use the sliding
window of size 16 frames over the video temporal to cut the video
clips with the stride as 8.

\subsection{Self-Attentive LSTM (S-LSTM) \label{subsec:Video-Level-Representation-by}}

We propose a self-attentive feature embedding to selectively learn
to compact feature representations. Such representations can efficiently
model the local information. Specifically, since each video has about
4300 frames with 2 minutes and 50 seconds duration, the total computational
cost of using all C3D features would be very heavy. On the other hand,
the trivial practice is to employ max or average pooling operator
to merge these features into video-level representations. However,
not all video clips/frames contribute equally to regressing the final
scores. Thus in order to extract a more compact feature representation,
we have to address two problems properly,
\begin{enumerate}
\item The features of clips that are important to difficulty technical movements
should be heavy weighted.
\item The produced compact feature representations should be the fixed length
for all the videos.
\end{enumerate}
To this end, a self-attentive embedding scheme is proposed here to
generate the video-level representations. In particular, suppose we
have a $T\times d$ dimensional \emph{C3D} feature sequence of a video
$F=\left(f_{1},f_{2},\cdots,f_{T}\right)$, we can compute the weight
matrix $A$,
\begin{equation}
A=\sigma_{1}\left(W_{s2}\sigma_{2}\left(W_{s1}F^{T}\right)\right)\label{eq:attention}
\end{equation}

\noindent where $\sigma_{1}\left(\cdot\right)$ and $\sigma_{2}\left(\cdot\right)$
indicates the softmax and hyperbolic tangent function respectively.
The $\sigma_{1}\left(\cdot\right)$ can ensure the computed weights
sum to 1.

We implement the Eq (\ref{eq:attention}) as a 2-layer Multiple Layer
Perceptron (MLP) without the bias of $d_{1}$ hidden neurons. Thus
the dimension of weights $W_{s1}$ and $W_{s2}$ are $d_{1}\times d$
and $d_{2}\times d_{1}$, and the dimension of $A$ is $d_{2}\times T$.
 The compact representation is computed as $M=A\cdot F$. Each row
of matrix $A$ can be interpreted as a specific focus point on the
video, maybe a key action pattern; the $d_{1}$ stands for the diversity
of descriptions. Therefore multiplying feature matrix $F$ with $A$
helps us extract all such patterns, resulting in a shorter input sequence,
with dimension of $d_{2}\times d$.

The resulting embedding $M$ is further followed by a 1-layer Long
Short-Term Memory (LSTM) with the $d_{2}$ LSTM cell. The output LSTM
is further connected to a 1-layer fully connected layer with 64 neurons
to regress the TES and PCS scores. We use the Mean Square Error (MSE)
as the loss function to optimize this self-attentive LSTM. A penalty
term is added to the MSE loss function in order to encourage the diversity
of learned self-attentive feature embedding $M$. The form of the
penalty is,
\begin{equation}
P=\left\Vert \left(AA^{T}-I\right)\right\Vert _{F}^{2}\label{eq:p}
\end{equation}

\noindent where $\left\Vert \cdot\right\Vert _{F}^{2}$ is the Frobenius
norm. $I$ is the identity matrix.

\begin{figure}
\centering{}\includegraphics[scale=0.46]{skip_lstm}\caption{\label{fig:skip}Structure of revised skip-LSTM cell. }
\end{figure}

The self-attentive LSTM is for the first time proposed here to address
the regression tasks. We highlight several differences with previous
works. (1) The attention strategy has been widely utilized in previous
works \cite{yunansigir2017,show2015tell,li2016videolstm,sermanet2014attention}.
In contrast, the self-attentive strategy simply uses the final output
of video sequences. Similar strategy has also been used in the NLP
tasks \cite{lin2017structured}. (2) Comparing with \cite{lin2017structured},
our self-attentive LSTM is also very different. The output of self-attentive
feature embedding is used as the input of LSTM and fully connected
layer for the regression tasks. In contrast, \cite{lin2017structured}
utilized the attention strategy to process the output of LSTM, and
directly concatenate the feature embedding for the classification
tasks.

\subsection{Multi-scale Convolutional Skip LSTM (M-LSTM) \label{subsec:Video-fusion-by}}

The self-attentive LSTM is efficient in modeling the local sequential
information. Nevertheless, it is essential to model the sequential
frames/clips containing the local (technical movements) and global
(performance of players), since in principle, TES scores the technical
movement, and the PCS reflects the whole performance of the player.
 In light of this understanding, we propose the multi-scale convolutional
skip LSTM (M-LSTM) model.

As an extension of LSTM, our M-LSTM learns to model the sequential
information at multiple scale. Specifically, the dense clip-based
C3D video features give the good representations of local sequential
information. To facilitate abstracting the information of multiple
scale, our M-LSTM employs several parallel $1$D convolution layers
with different kernel sizes, as shown in Fig. \ref{fig:model}.
The kernel with small size of filters can aggregate and extract the
visual representation of action patterns lasting seconds in the videos.
The kernel of large size of filters will try to model the global information
of the videos. However, in practice, quite different from the videos
used for video classification (e.g., UCF101 \cite{Soomro2012}), our
figure skating videos are quite longer. Thus the total frames of our
figure staking videos still make the training process of LSTM difficulty
in capturing long term dependencies.

To solve this issue, we further propose the skipping RNN strategy
here. Particularly, we propose the revised skip LSTM structure. An
origin LSTM works as follows:
\begin{align}
i_{t},f_{t},o_{t}= & \sigma\left(W_{x}x_{t}+W_{h}h_{t-1}+b\right)\label{eq:ifo}\\
g_{t}= & tanh\left(W_{xg}x_{t}+W_{hg}h_{t-1}+b_{g}\right)\label{eq:g}\\
c_{t}= & f_{t}\odot c_{t-1}+i_{t}\odot g_{t}\label{eq:c}\\
h_{t}= & o_{t}\odot tanh\left(c_{t}\right)\label{eq:h}
\end{align}

\noindent where $i_{t},f_{t},o_{t}$ are the input, forget and output
gates. $\sigma\left(\cdot\right)$ indicates sigmod function. $x_{t}$
is the input of LSTM; the hidden state and cell state of LSTM are
denoted as $h_{t}$ and $c_{t}$ respectively. $W_{x},W_{h}$ $W_{xg},W_{hg}$
and $b,b_{g}$ are the learning weights of parameters. In skip LSTM,
a binary state update gate, $u_{t}\in\left\{ 0,1\right\} $ is added,
which is used to control the update of cell state and hidden state.
The whole new update rule is as follows,
\begin{align}
u_{t} & =f_{binary}\left(\tilde{u}_{t}\right)\label{eq:u_skip}\\
i_{t},f_{t},o_{t} & =\sigma\left(W_{x}x_{t}+W_{h}h_{t-1}+b\right)\\
g_{t} & =tanh\left(W_{xg}x_{t}+W_{hg}h_{t-1}+b_{g}\right)\\
\tilde{c_{t}} & =f_{t}\odot c_{t-1}+i_{t}\odot g_{t}\\
\tilde{h_{t}} & =o_{t}\odot tanh\left(c_{t}\right)\\
c_{t} & =u_{t}\cdot\tilde{c}_{t}+\left(1-u_{t}\right)\cdot c_{t-1}\\
h_{t} & =u_{t}\cdot\tilde{h}_{t}+\left(1-u_{t}\right)\cdot h_{t-1}\\
\Delta\tilde{u}_{t} & =\sigma\left(W_{p}c_{t}+b_{p}\right)\\
\tilde{u}_{t+1} & =u_{t}\cdot\Delta\tilde{u}_{t}+\left(1-u_{t}\right)\cdot\left(\tilde{u}_{t}+\min\left(\Delta\tilde{u}_{t},1-\tilde{u}_{t}\right)\right)
\end{align}

\noindent Where $\sigma\left(\cdot\right)$ is sigmoid function,
$\odot$ denotes element-wise multiplication. $f_{binary}\left(\cdot\right)$
indicates the round function. $\tilde{c}_{t}$ and $\tilde{h_{t}}$
are the values of corresponding state $c_{t}$ and $h_{t}$ if $u_{t}=1$.
$\Delta\tilde{u}_{t}$ is the accumulated error if not updating the
control variable $u_{t}$. Furthermore, different from \cite{campos2017skip},
our model revises the update rule of $c_{t}$ and $h_{t}$ to prevent
the network from being forced to expose a memory cell which has not
been updated, which would result in misleading information, as shown
in Fig. \ref{fig:skip}.
\begin{align}
c_{t} & =f_{t}\odot c_{t-1}+u_{t}\cdot i_{t}\odot g_{t}\label{eq:ct}\\
h_{t} & =\left(\left(1-u_{t}\right)\cdot o_{t}+u_{t}\cdot o_{t-1}\right)\odot tanh\left(c_{t}\right)\label{eq:ht}
\end{align}

The key ingredient of our skip LSTM lies in Eq (\ref{eq:u_skip}).
By using the round function, our M-LSTM can skip some less significant
update if $u_{t}=0$. By virtue of this way, our M-LSTM can model
even longer term data dependencies.

The whole structure of our M-LSTM is also illustrated in Fig. \ref{fig:model}.
Since the skip LSTM is used to discard redundant information, we only
connect it to the convolution layers with small-size kernels, and
apply the common LSTM after other convolution layers. The outputs
at the final time-step of all parallel LSTMs are then concatenated
and transmitted to a fully connected layer to regress the prediction
scores. \textcolor{red}{ }

Thus, with this M-LSTM architecture, we can actually have the best
of both world: the multi-scale convolutional structures can extract
the local and global feature representations from videos; the revised
skip LSTM can efficiently skip/discard the redundant information that
is not essential in learning the local and global information. The
final LSTM outputs of different scales are still concatenated and
learned by the nonlinear fully connected layer for the regression.
The effectiveness of our M-LSTM is validated in the experiments.

\section{Experiments\label{sec:Experiments}}

\subsection{Settings and Evaluation}

\noindent \textbf{Datasets. }We evaluate our tasks in both MIT-skate
\cite{quality_action} and our Fis-V dataset. MIT-skate has 150 videos
with 24 frames per second. We utilize the standard data split of 100
videos for training and the rest for testing. In our Fis-V dataset,
we introduce the split of 400 videos as training, the rest as testing.

\vspace{0.1in}

\noindent \textbf{Metric Settings}. As for the evaluation, we use
the standard evaluation metrics, the spearman correlation \textcolor{red}{$\rho$
}\textendash{} proposed in \cite{quality_action} and \cite{parmar2017learning}.
This makes the results of our framework directly comparable to those
results reported in \cite{quality_action,parmar2017learning}. Additionally,
to give more insights of our model, the Mean Square Error (MSE) is
also utilized here to evaluate the models. In MIT-skate, the published
results are trained on the final scores; so these scores have been
used to evaluate our framework.

\vspace{0.1in}

\noindent \textbf{Experimental Settings}. For self-attention LSTM
subnetwork, we set $d_{1}=1024,d_{2}=40$, and the hidden size of
LSTM is also set as $256$. The batch size is 32. For M-LSTM subnetwork,
we use the hidden size of $256$ for both types of LSTM layers, the
other parameter setting is depicted in Fig. \ref{fig:model}. For
both models we use a two-layer perceptron with a hidden size of 256
and ReLU function as the activation function of hidden layer. We build
both models by Pytorch and optimize the model by Adam \cite{kingma2014adam}
algorithm with learning rate of $1e-4$. The whole framework is trained
on 1 NVIDIA 1080Ti GPU card and can get converged by 250 epochs. It
totally takes 20 minutes to train one model. We augment the videos
by the horizontal flipping on frames. As the standard practice, the
Dropout is set as 0.7 and only used in fully connected layers; batch
normalization is added after each convolution layer in our model.
Our model is an end-to-end network; so, we directly use the C3D feature
sequences of training data to train the model with the parameters
above.

Addtitionally, we donot fine-tune the C3D features in our model, due
to the tremendous computational cost. Literally, our videos are very
long (averagely 4400 frames), but only around 400 videos. So if we
want to finetune C3D with Fis-V or MIT-skate dataset, we need to forward
pass and backpropagate on this relatively large C3D model 400 iteration
for each video. This requires huge computational cost. On the other
hand, we have observed overfitting in our training if the hyperparameter
is not well tuned, due to the small dataset size (only 400 videos).
Thus adding C3D into training graph would make the training process
more difficult.

\vspace{0.1in}

\noindent \textbf{Competitors.} Several different competitors and
variants are discussed here. Specifically, we consider different combinations
of the following choices:
\begin{enumerate}
\item Using frame-level features: We use the 2048 dimensional feature from
the pool5 layer of the SENet \cite{SEnet}, which is the winner of
ILSVRC 2017 Image Classification Challenge.
\item Using max or average pooling for video-level representation.
\item Using different regression models: SVR with linear or RBF kernels
are utilized for regression tasks.
\item LSTM and bi-LSTM based models. We use the C3D-LSTM model depicted
in \cite{parmar2017learning}. Note that due to very long video sequence
and to make a more fair comparison, we set the hidden size of LSTM
as $256$, adopt an option of bi-directional LSTM, and use a multi-layer
regressor same as our models. This C3D-LSTM model is extended to using
SENet features, or by using bi-directional LSTM.
\item \cite{quality_action,hierarchical}. We also report the results of
these two papers.
\end{enumerate}

\subsection{Results}

\begin{table}
\begin{centering}
\begin{tabular}{c|c|c|c|c|c}
\hline
\multirow{2}{*}{Features} & \multirow{2}{*}{Pooling} & \multirow{2}{*}{Reg} & \multirow{2}{*}{MIT-skate} & \multicolumn{2}{c}{Fis-V}\tabularnewline
\cline{5-6}
 &  &  &  & TES & PCS \tabularnewline
\hline
\hline
\cite{quality_action} & \multicolumn{2}{c|}{\textemdash{}} & 0.33 & \textendash{} & \textendash{}\tabularnewline
\hline
\cite{hierarchical} & \multicolumn{2}{c|}{\textemdash{}} & 0.45 & \textendash{} & \textendash{}\tabularnewline
\hline
\cite{parmar2017learning} & \multicolumn{2}{c|}{\textemdash{}} & 0.53 & \textendash{} & \textendash{}\tabularnewline
\hline
\hline
\multirow{6}{*}{SENet} & Max & Linear & \textendash{} & 0.39 & 0.53\tabularnewline
\cline{2-6}
 & Avg & Linear & \textendash{} & 0.43 & 0.61\tabularnewline
\cline{2-6}
 & Max & RBF & \textendash{} & 0.27 & 0.43\tabularnewline
\cline{2-6}
 & Avg & RBF & \textendash{} & 0.21 & 0.34\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{LSTM} & \textendash{} & 0.57 & 0.70\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{bi-LSTM} & \textendash{} & 0.57 & 0.70\tabularnewline
\hline
\multirow{6}{*}{C3D} & Max & Linear & 0.48 & 0.47 & 0.61\tabularnewline
\cline{2-6}
 & Avg & Linear & 0.40 & 0.40 & 0.59\tabularnewline
\cline{2-6}
 & Max & RBF & 0.44 & 0.35 & 0.49\tabularnewline
\cline{2-6}
 & Avg & RBF & 0.42 & 0.41 & 0.56\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{LSTM} & 0.37 & 0.59 & 0.77\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{bi-LSTM} & 0.58 & 0.56 & 0.73\tabularnewline
\hline
\hline
\multirow{3}{*}{C3D} & \multicolumn{2}{c|}{M-LSTM} & 0.56 & 0.65 & 0.78\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{S-LSTM} & 0.51 & \textbf{0.67} & 0.77\tabularnewline
\cline{2-6}
 & \multicolumn{2}{c|}{S-LSTM+M-LSTM} & \textbf{0.59} & 0.65 & \textbf{0.78}\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{\label{table:regression}Results of the spearman correlation (the
higher the better) on MIT-Skate and Fis-V. ``S-LSTM'' is short for
self-attentive LSTM. ``M-LSTM'' is short for Multi-scale Convolutional
Skip LSTM used.}
\end{table}
\begin{table}
\begin{centering}
\begin{tabular}{c|c|c|c|c}
\hline
\multirow{1}{*}{Features		} & \multirow{1}{*}{Pooling} & \multirow{1}{*}{Reg} & TES & PCS\tabularnewline
\hline
\hline
\multirow{6}{*}{SENet} & Max & Linear & 32.35 & 16.14\tabularnewline
\cline{2-5}
 & Avg & Linear & 34.15 & 15.15\tabularnewline
\cline{2-5}
 & Max & RBF & 37.78 & 22.07\tabularnewline
\cline{2-5}
 & Ag & RBF & 39.69 & 24.70\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{LSTM} & 23.38 & 11.03\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{bi-LSTM} & 24.65 & 11.19\tabularnewline
\hline
\multirow{6}{*}{C3D} & Max & Linear & 27.42 & 13.98\tabularnewline
\cline{2-5}
 & Avg & Linear & 30.25 & 15.96\tabularnewline
\cline{2-5}
 & Max & RBF & 40.19 & 25.13\tabularnewline
\cline{2-5}
 & Avg & RBF & 34.60 & 19.08\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{LSTM} & 22.96 & 8.70\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{bi-LSTM} & 23.80 & 10.36\tabularnewline
\hline
\hline
\multirow{3}{*}{C3D} & \multicolumn{2}{c|}{M-LSTM} & 19.49 & 8.41\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{S-LSTM} & \textbf{19.26} & 8.53\tabularnewline
\cline{2-5}
 & \multicolumn{2}{c|}{S-LSTM+M-LSTM} & 19.91 & \textbf{8.35}\tabularnewline
\hline
\end{tabular}
\par\end{centering}
\caption{\label{table:regression-1}Results of the MSE on Fis-V. }
\end{table}

\begin{figure*}
\begin{centering}
\includegraphics[scale=0.35]{Fig6}
\par\end{centering}
\caption{\label{fig:visualize}Qualitative study on self-attention model. The
top two rows show a clip with high attention weights while the bottom
two rows show clips of low attention weights. }
\end{figure*}

\vspace{0.1in}

\noindent \textbf{Results of the spearman correlation.} We report
the results in Tab. \ref{table:regression}. On MIT-skate and our
Fis-V dataset, we compare several variants and baselines. We highlight
that our framework achieves the best performance on both datasets,
and outperform the baselines (including \cite{quality_action,hierarchical,parmar2017learning})
clearly by a large margin. This shows the effectiveness of our proposed
framework. We further conduct the ablation study to explore the contributions
of each components, namely, M-LSTM and S-LSTM. In general, the results
of M-LSTM can already beat all the other baselines on both datasets.
This is reasonable, since the M-LSTM can effectively learn the local
and global information with the efficient revised skip LSTM structure.
Further, on MIT-skate dataset the S-LSTM is complementary to M-LSTM,
since we can achieve higher results. The performance of M-LSTM and
S-LSTM is very good on Fis-V dataset.

\vspace{0.1in}

\noindent \textbf{Results of different variants.} As the regression
tasks, we further explore different variants in Tab. \ref{table:regression}.
By using the C3D features, we compare different pooling and Regression
methods.

(1) \emph{Max Vs. Avg} \emph{pooling}. Actually, we donot have conclusive
results which pooling method is better. The max pooling has better
performance than average pooling on MIT-skate dataset, while the average
pooling can beat the maximum pooling on Fis-V dataset. This shows
the difficult intrinsic of the regression tasks.

(2) \emph{RBF Vs. Linear SVR}. In general, we found that the linear
SVR has better performance than the RBF SVR. And both methods have
lower performance than our framework.

(3) \emph{SENet Vs. C3D.} On Fis-V dataset, we also compare the results
of using SENet features. SENet are the static frame-based features,
and C3D are clip-based features. Note that within each video, we generally
extract different number of SENet and C3D features; thus it is nontrivial
to directly combine two types of features together. Also the models
using C3D features can produce better prediction results than those
from SENet, since the figure skating videos are mostly about the movement
of each skater. The clip-based C3D features can better abstract this
moving information from the videos.

(4) \emph{TES Vs. PCS}. With comparable models and features, the correlation
results on PCS are generally better than those of TES. This reflects
that the PCS is relatively easier to be predicted than TES.

\vspace{0.1in}

\noindent \textbf{Results of the mean square error.} On our Fis-V
dataset, we also compare the results by the metrics of MSE in Tab.
\ref{table:regression-1}. In particular, we find that the proposed
M-LSTM and S-LSTM can significantly beat all the other baseline clearly
by a large margin. Furthermore, we still observe a boosting of the
performance of PCS by combining the M-LSTM and S-LSTM.

Interestingly, we notice that on TES, the combination of S-LSTM+M-LSTM
doesnot have significantly improved over the M-LSTM or S-LSTM only.
This is somehow expected. Since the TES task aims at scoring those
clips of technical movements. This is relatively a much easier task
than the PCS task which aims at scoring the good interpretation of
music. Thus only features extracted by M-LSTM or S-LSTM can be good
enough to learn a classifier for TES. The combination of both M-LSTM
and S-LSTM may lead to redundant information. Thus the S-LSTM+M-LSTM
can not get further improvement over M-LSTM or S-LSTM on TES.

\vspace{0.1in}

\noindent \textbf{Ablation study on Self-Attentive strategy. }To
visualize the self-attentive mechanism, we compute the attention weight
matrix $A$ of a specific video. We think that if a clip has high
weight in at least one row of $A$, then it means that this clip shows
an important technical movement contributing to the TES score, otherwise
it is insignificant. We show a pair of example clips (16 frames) in
Fig. \ref{fig:visualize}. From Fig. \ref{fig:visualize} we can find
the clip in the top two rows with higher attention weights is showing
``hard'' action, for instance, ``jumping on the same foot within
a spin'', while movement in the bottom clips would not.\textcolor{red}{{}
}

\section{Conclusion\label{sec:Conclusion}}

In this paper, we present a new dataset \textendash{} Fis-V dataset
for figure skating sports video analysis. We target the task of learning
to score of each skater's performance. We propose two models for the
regression tasks, \emph{namely}, the Self-Attentive LSTM (S-LSTM)
and the Multi-scale Convolutional Skip LSTM (M-LSTM). We also integrate
the two proposed networks in a single end-to-end framework. We conduct
extensive experiments to thoroughly evaluate our frameworks as well
as the variants on MIT-skate and Fis-V dataset. The experimental results
validate the effectiveness of proposed methods.

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


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