The United Nations General Assembly proclaimed that 1981 as the International Day of Disabled Persons, in 1976. It called for an action plan at the national and international levels, government and organizations to enforce measures to make better the lives of disabled persons all over the world. The theme of International Day of Disabled Persons was "full participation and equality," defined as the rights of those people who are living disabilities to take part fully in the life and development of their society and enjoy all living conditions equal to those of other citizens. The United Nation General Assembly proclaimed that 3 December as the International Day of Disabled Persons on October 14, 1992. This day was first observed on 3 December 1992. On 18 December 2007, the United Nation Assembly changed the name of "International Day of Disabled Persons" to "International Day of Persons with Disabilities." After this, the new name was used to promote the day.
To promoting International Day of Persons with Disabilities people of many countries from the world participate in various activities and events. In these events include the art exhibition to promoting artwork and other events of protests to highlight the difficulties of disabled people who play a full role in society.
On International Day of Persons with Disabilities in 2012, the United Kingdom government introduced the essential work for disabled people who received welfare benefits to "Improve disabled people's chances of getting work by mandatory employment." It is essential right to participate in public life to create stable democracies, active citizenship and lessen inequalities in society.
The United Nation's International Day of Persons with Disabilities is an annual observance and not a Public holiday. All business and educational institutes are open as usual. Public Life is only affected when some marches and rails come out.
The theme of this year is to focus on empowering persons with disabilities for comprehensive, equitable and sustainable development as part of the 2030 Agenda for Sustainable Development. On the International Day of Persons with Disabilities, we ought to work together with United Nations to promote and support the rights and dignity of people in all spheres of society who live with disabilities, and to raise awareness of the situation of persons with disabilities in every aspect of political, social, economic and cultural life. It is an opportunity to enhance their capacities and supports them in setting their priorities. When people are empowered they are better prepared to take benefit of opportunities, they become representative of change and can more happily embrace their responsibilities.


text:
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Posted By: Kaylee BrewsterPosted date: March 19, 2020
Top streams for the week
To ease our travel into the unknown in the real world, Disney+ makes "Frozen II" (2019, PG) available to subscribers months ahead of schedule. The animated musical fantasy brings Elsa, Anna, and friends back for a new quest with a more dramatic story and greater stakes, and a fine score that doesn't quite match the musical heights of the original. It was an Oscar nominee for its centerpiece original song. Features the voices of Kristen Bell, Idina Menzel, Jonathan Groff, and Josh Gad, with newcomers Sterling K. Brown and Evan Rachel Wood.
Octavia Spencer plays C.J. Walker, the African-American businesswoman who built a business empire pioneering hair care products for black women at the turn of the 20th century, in "Self Made: Inspired by the Life of Madam C.J. Walker" (2020, not rated), a biographical drama that offers an eye-opening portrait of black life in 19th century America. Carmen Ejogo and Tiffany Hadish costar in the three-hour, four-part limited series now streaming on Netflix.
Reese Witherspoon and Kerry Washington spark "Little Fires Everywhere" (2020, TV-MA) in the eight-part adaptation of Celeste Ng's novel of class and power in 1990s Shaker Heights, Ohio. Controlling suburban mother Witherspoon and artist and single mother Washington clash as their lives become entwined. Rosemarie DeWitt and Joshua Jackson costar, and Seattle filmmaker Lynn Shelton directs the first and final episodes of the limited series. Three episodes are now streaming on Hulu, with new episodes each Wednesday.
"The Plot Against America" (2020, TV-MA), based on Phillip Roth's alternate history novel of America under President Charles Lindberg, presents a 1940s America in which intolerance and anti-Semitism takes hold. The American aviation hero also was virulently anti-Semitic and objected to America's involvement in World War II, and the series dramatizes a terrifying turn to nationalism, bigotry and anti-Jewish violence in 1940s New York. David Simon, the creator of "The Wire" and "The Deuce," adapts and produces, and Zoe Kazan, Morgan Spector, Winona Ryder and John Turturro star. It's available n all HBO platforms, with new episodes airing Monday nights.
Canadian comedian Mae Martin plays a version of herself embarking on a new relationship in London in "Feel Good: Season 1" (not rated), a comedy that takes on LGBTQ relationships, gender identity and addiction. Six episodes are available on Netflix.
Essie Davis returns as TV's most glamorous high-society detective in "Miss Fisher and the Crypt of Tears" (2020, not rated), the feature film sequel to the hit Australian mystery series. The globetrotting adventure revolves around a curse and takes her to Jerusalem, London and Egypt, where she is never less than perfectly decked out in high fashion. It debuts on Acorn TV after a limited theatrical run.
With movie theaters closing across the country, Universal is making three new releases available on Cable On Demand and VOD: the witty Jane Austen adaptation "Emma." (2020, PG); the modern reworking of "The Invisible Man" (2020, R) with Elisabeth Moss; and the divisive satirical thriller "The Hunt" (2020, R). Expect premium prices for these offerings. I anticipate other studios will follow suit with select movies, but don't expect to see major big-budget releases, which are being rescheduled for release later in the year.
Pay-Per-View / Video on Demand
"Richard Jewell" (2019, R), Clint Eastwood's drama about the security guard who was crucified in the media on unfounded charges, got lost in the winter movie season but earned an Oscar nomination for actor Kathy Bates. Paul Walter Hauser stars with Sam Rockwell, Jon Hamm and Olivia Wilde. It's also on DVD and at Redbox. Also new:
Terrence Malick's "A Hidden Life" (2019, R), based on the true story of an Austrian farmer who refused to fight for the Nazis in World War II;
action fantasy "Jumanji: The Next Level" (2019, PG-13) with Dwayne Johnson, Jack Black, Kevin Hart and Karen Gillan; amd
"Seven Stages to Achieve Eternal Bliss" (2018, not rated), a dark comedy starring Kate Micucci, Sam Huntington and Taika Waititi.
The seven-episode limited series "Tiger King" (2020, not rated) is a stranger-than-fiction true crime documentary about a murder in the world of wild cat breeders.
Think of the new fantasy adventure "The Letter for the King: Season 1" (2020, TV-PG), adapted from a beloved Dutch young adult novel, as "Game of Thrones" for kids.
"The English Game" dramatizes the transformation of English football from an elite sport of gentlemen to one dominated by working-class players. "Downton Abbey" creator Julian Fellowes scripts the six-part limited series.
"Altered Carbon: Resleeved" (2020, not rated) is an animated feature spin-off of the live-action series.
More true stories: Also new: "A Life of Speed: The Juan Manuel Fangio Story" (2020, not rated) looks at the five-time Formula One champion of the early 1950s.
International cinema: "The Platform" (Spain, 2019, not rated, with subtitles) is a prison drama that combines allegory and social commentary.
Jennifer Lawrence won an Oscar for "Silver Linings Playbook" (2012, R), the offbeat romantic comedy with Bradley Cooper and Robert De Niro.
Gary Oldman is George Smiley in "Tinker Tailor Soldier Spy" (2011, R), the Oscar-nominated adaptation of the John le Carré novel.
Kid stuff: the animated "Shaun the Sheep – Adventures from Mossy Bottom: Season 1" (2020) arrives from Aardman Animations. For the very young, there's "Buddi: Season 1" and for teens and tweens there's the feature film adolescent fantasy "Lu Over the Wall" (Japan, 2017, PG) and high school mystery "Greenhouse Academy: Season 4." There are also new seasons of the animated shows "The Boss Baby: Back in Business," "Archibald's Next Big Thing," and "Dino Girl Gauko."
Streaming TV: High school sports drama "All American: Season 2" (2019-20, TV-14) and superhero series "Black Lightning: Season 3" (2019-20, TV-14) are available, barely a week after concluding on CW.
And if you really need to indulge in an end-of-the-world film, the epic disaster spectacle "2012" (2009, PG-13) with John Cusack and Amanda Peet is new to Netflix.
Stand-up: "Bert Kreischer: Hey Big Boy" (2020, not rated).
Two grieving teenage sisters (Morgan Saylor and Sophie Lowe) uncover the dark secrets of their small Maine town in the Amazon Original movie "Blow the Man Down" (2020, R), a dark crime comedy. Bridget Savage Cole and Danielle Krudy write and direct the noirish American indie, and Margo Martindale costars.
Jim Henson combines fairy tale and medieval legend in "The Storyteller" (1989), narrated by John Hurt, and reaches back even further for "The Storyteller: Greek Myths" (1991) with Michael Gambon. Anthony Minghella (who went on to write and direct "The English Patient") scripts the first series, and both shows feature a mix of human actors and sophisticated puppetry and celebrate stories and the art of storytelling with grace and imagination. Both are family friendly.
Kid stuff: Jack Black is the voice of "Kung Fu Panda" (2008, PG) in the original animated adventure comedy with Dustin Hoffman and Angelina Jolie.
"SNL" regular Pete Davidson plays a fun-loving screw-up and catastrophic role-model to an impressionable 15-year-old (Griffin Gluck) in the coming-of-age comedy "Big Time Adolescence" (2020, TV-MA).
The Freeform series "Motherland: Fort Salem" (2020, not rated), is set in an alternate reality where the descendants of Salem witches are trained to become weapons. It rolls out on Hulu with new episodes each Thursday.
More streaming TV: "The Walking Dead" spinoff "Fear the Walking Dead: Season 5" (2019, TV-MA) now streams in its entirety.
True stories: "Margaret Atwood: A Word after a Word after a Word is Power" (2019, not rated) presents the Canadian author discussing her life and work.
Mackenzie Davis and Caitlin FitzGerald play actresses trying to reconnect after falling out in the psychological drama "Always Shine" (2016, unrated).
The third and final season of the science fiction thriller "Westworld" (TV-MA) moves out of the high-tech theme park and into the material world as Dolores (Evan Rachel Wood), the sophisticated robot that developed sentience and a survival instinct, takes on humanity. Jeffrey Wright, Thandie Newton and Tessa Thompson return to the heady, high-concept series, which takes on a sleek new style.
Also returning is "My Brilliant Friend: Season 2" (Italy, TV-MA, with subtitles), based on the novels by Elena Ferrante.
Dwayne Johnson and Jason Statham take the lead in "Fast & Furious Presents: Hobbs & Shaw" (2019, PG-13), the buddy film spinoff of the hit action franchise.
True stories: The subject of "After Truth: Disinformation and the Cost of Fake News" (2020, TV-MA) is all in the title.
Other streams
Anthony Mackie and Samuel L. Jackson star as African-American businessmen who hatch a business plan to fight for housing integration in the 1960s in "The Banker" (2020, PG-13), a drama based on a true story. It comes to Apple TV+ a few weeks after debuting in theaters.
The second season of "Black Monday" (TV-MA), a comedy set in the Wall Street crash of the 1980s, begins on Showtime Anytime. New episodes arrive on Sundays.
The new season of the Irish crime drama "Blood"(2020, not rated), with Adrian Dunbar; and two more seasons of the 1980s series "Lovejoy," with Ian McShane as a charming rogue of an antique hunter, are available on Acorn TV.
"Felix" (Spain, 2018, with subtitles), a quirky crime miniseries starring Leonardo Sbaraglia; and crime drama "The Hunters: Season 1" (Sweden, 2018, with subtitles) starring Rolf Lassgård, are streaming on MHz. New episodes of both shows arrive on Tuesdays.
Criterion Channel presents seven films in "Starring Rita Hayworth." Hayworth was dubbed "The Love Goddess" of 1940s Hollywood. The series includes the Howard Hawks adventure drama "Only Angels Have Wings" (1939) with Cary Grant and Jean Arthur; the musical "Cover Girl" (1944) with Gene Kelly; and Hayworth's great film noirs "Gilda" (1946) and "The Lady from Shanghai" (1947), directed by and starring Orson Welles. Also new this month:
Kathleen Collins' independent drama "Losing Ground" (1982, not rated), the first feature directed by an African-American woman;
impressionistic, immersive documentaries "Sweetgrass" (2009, not rated) and "Leviathan" (2012, not rated) from Harvard University's Sensory Ethnography Lab; and
Michelangelo Antonioni's "The Passenger" (Italy, 1975, PG-13), starring Jack Nicholson.
New on disc and available at Redbox
"Richard Jewell," "Jumanji: The Next Level" and "Black Christmas"
Axmaker is a Seattle film critic and writer. His reviews of streaming movies and TV can be found at http://streamondemandathome.com.
A notetaker's narrative of Tiananmen Square: Novel gives insights into China's recent history
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"Mao's Kisses: A Novel of June 4, 1989" By Alex Kuo 2019, Redbat Books, LaGrande, Ore. Paperback, 187...

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Feel free to contact Joanne Gobeil Psychological Services Inc. with any questions/comments that you may have in regards to counselling or assessments, or for a complimentary intake call. Alternatively, you may choose to use the contact form on this page.
Suite #5, 118 Main Street N.E.


Love & Pineapples is a clothing line for girls who believe strong is always in style and being spontaneous just makes life sweeter! I designed apparel graphics for their 2017 line. Each graphic highlights their southern preppy charm and aesthetic.


Glass bottle with a small amount of antimonial wine and stained, printed label. Part of George III mahogany apothecary's box.
Glasgow Apothecaries Company; 1805-1919, "Antimonial wine," Heritage, accessed April 23, 2019, https://heritage.rcpsg.ac.uk/items/show/795.
Description: Glass bottle with a small amount of antimonial wine and stained, printed label. Part of George III mahogany apothecary's box.


Here you will find one or more explanations in English for the word Lindera. Also in the bottom left of the page several parts of wikipedia pages related to the word Lindera and, of course, Lindera synonyms and on the right images related to the word Lindera.
Spicewood Spice"wood`, n. (Bot.) An American shrub (Lindera Benzoin), the bark of which has a spicy taste and odor; -- called also Benjamin, wild allspice, and fever bush.
Allspice All"spice`, n. The berry of the pimento (Eugenia pimenta), a tree of the West Indies; a spice of a mildly pungent taste, and agreeably aromatic; Jamaica pepper; pimento. It has been supposed to combine the flavor of cinnamon, nutmegs, and cloves; and hence the name. The name is also given to other aromatic shrubs; as, the Carolina allspice (Calycanthus floridus); wild allspice (Lindera benzoin), called also spicebush, spicewood, and feverbush.
Benzoin Ben*zoin" (b[e^]n*zoin"), n. [Cf. F. benjoin, Sp. benjui, Pg. beijoin; all fr. Ar. lub[=a]n-j[=a]w[=i] incense form Sumatra (named Java in Arabic), the first syllable being lost. Cf. Benjamin.] Note: [Called also benjamin.] 1. A resinous substance, dry and brittle, obtained from the Styrax benzoin, a tree of Sumatra, Java, etc., having a fragrant odor, and slightly aromatic taste. It is used in the preparation of benzoic acid, in medicine, and as a perfume. 2. A white crystalline substance, C14H12O2, obtained from benzoic aldehyde and some other sources. 3. (Bot.) The spicebush (Lindera benzoin). Flowers of benzoin, benzoic acid. See under Benzoic.


Enjoy comfort all day with this ROMIKA walking boot. Made by this trusted European brand using TopDry Tex so your feet stay dry and warm while you're on the go. Strong laces and ski boot style eyelets make this boot robust for winter weather. Detailed top stitching gives this boot a Nordic, slightly retro look. Plush lining for warmth and comfort. Good grip on a lightweight sole. Leather upper.


text:
Q: Solving $\int \sqrt {f(x)} \ dx=\sqrt{\int f(x)\ dx}$ I am trying to solve the following differential equation:

$$\int \sqrt {f(x)} \ dx=\sqrt{\int f(x)\  dx}$$

My approach:
I started by differentiating both sides and got:
$$\sqrt {f(x)}=f(x) \frac{1}{2\sqrt{\int f(x)\  dx}}$$
We can isolate the $2\sqrt{\int f(x)\  dx}$ in the left side:
$$2\sqrt{\int f(x)\  dx} = \sqrt{f(x)}$$
We can now square both sides:
$$4\int f(x)\  dx = f(x) \ \ \ \ \ \ \ \ \ (1)$$
We can again differentiate both sides and get:
$$4f = f'$$
And the solution of this equation is: $f(x)= Ce^{4x}$
My question is the following:
In the step where I putted a (1), it should be
$$4 \left|\int f(x)\  dx\right| = |f(x)|$$
Instead of $4\int f(x)\  dx = f(x)$, because $\sqrt{a}^2 = |a|$. How could I solve this differential equation if I did not make the assumption that $f(x) > 0$ and $\int f(x) dx > 0$ and got rid of the modulus?

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text:
Berghahn Journals
About Subject Collections
Advances in Research
Licensing Agreements
OPEN ANTHRO
Berghahn Open Anthro
The Indigenisation of Allopathic Medicine in Iran
in Anthropology of the Middle East
Author: Agnes G. Loeffler 1
1 University of Wisconsin-Madison ALoeffler@uwhealth.org
https://doi.org/10.3167/ame.2008.030206
This article offers an analysis of two medical case histories presented by an Iranian allopathic physician to illustrate the power of diet in the management of disease. Uncovering underlying cultural assumptions about health and health maintenance strategies leads to the following insights: (1) Galenic medical ideas have not been replaced by allopathic theories in the world view of Iranian physicians; (2) allopathic medical treatment options (pharmaceuticals) are applied to indigenous disease categories; (3) there is deep-seated scepticism about etiologic theories of allopathic medicine and its ability to treat certain conditions; (4) the authority of allopathic medicine is not unquestioned in Iran.
Anthropology of the Middle East
allopathic medicine; constitution; diet; disease; health; Iran; medical anthropology
© Berghahn Books 2008
Issue Table of Contents
Sports Diplomacy and Emergent Nationalism
Polygyny and Post-nomadism among the Bedouin in Israel
Negotiating Honour and Shame in the Contemporary Moroccan Rif
The Painful Return
Kuwait and Iran
Article by Agnes G. Loeffler
© 2019 Berghahn Books


Sangak is an oblong Iranian flat bread made to be about 2 feet (60 cm) long.
About 45% of the flour used in Iran goes into making Sangak. It is made from wheat flour – 3 parts whole wheat to 1 part white flour.
The dough is a mild yeast. A portion of yeast saved from the previous day is used to leaven the current day's yeast. It is allowed to ferment for 1 1/2 hours, then rolled, shaped, sprinkled with sesame seeds and baked for around 5 minutes on top of small pebbles in an oven.
When done, the pebbles are shaken off, leaving indents on the bottom where the dough rested on them. Sometimes in the Middle East, you might still find tiny stones stuck to the bottom of the bread.
Sangak Bread is best hot or warm and has a short shelf life needing to be baked each day.
For weddings and New Years Sangak Bread is often served decorated.
At Taaj Market and Restaurant, we have different ways we can make the bread so there are certain subtleties in the flavor of the bread. We certainly have customers visiting our restaurant to try the bread and take some home, and we are sure you will too, once you try it!


My plan is to post chapter one of Serpent's Smile — yes, the novel now has a title — in the wee hours of Saturday morning. You'll be able to enjoy it, maybe, with your morning coffee.
I say "maybe" because the murder the hero witnesses is bloody, grisly and shocking.
In the fiction writing business, Chapter One is typically intended to move the reader from the rack to the cash register. It's supposed to be a "grabber" that, once read, compels the reader to buy the book.
If I've succeeded, you'll want to read Chapter Two which will become available the following Saturday morning. That process will continue, one chapter each Saturday, until all but the climactic ending have been posted.
At that point, unfortunately, a decision must be made: whether to post the climax on the website free of charge, or to make it available only in a Kindle or other (inexpensive) eBook publication.
I don't know the answer to the question of an eBook or a free edition, so for the time being, I'm keeping all options open.
Regardless, I do promise the ending will be thrilling! It involves a very tall building in an exotic location, a more than trivial amount of high explosives, the bad guy holding the beautiful romantic interest in great danger, and some very risky heroics by, of course, the hero — but you could probably predict that.
What you don't know, of course, is how it actually works out.
I have three endings I'm exploring. In one, the girl saves our hero but sacrifices her own life — my wife doesn't like that one but, hey, it's very noble of her. In another, the hero plugs the bad guy smack dab in the middle of his forehead — the hero is, after all, a Bullseye pistol shooter. And in the third alternative climactic ending, well, you'll just have to wait and see.
Look for chapter one, the grabber, this Saturday morning (midday for Europeans).


text:
Q: is Command appropriate software pattern for this? I have following task:
I need to send several instructions after each other to the hardware. Next instruction is never sent before either answer to previous instruction comes (valid answer or error) or timeout happens.
Currently all of those instruction has their own listeners with onReceived(answer) and onError(ErrorCode), and next instruction is called from onReceived of previous.. As you can imagine this code is not optimal and by adding new instruction to the sequence, class get even more cluttered and it is harder to maintain or debug.
My goal would be to have some kind of simple class that will have list of instructions in correct order and every instruction to have way to process it's own received answer and store it in common place. This simple manager class would just call execute() or next() and correct instruction would be sent, answer would be stored if correct or command repeated certain number of times in case of error.
Command pattern seems to be way to go but I am not sure if I am on the right track and how to react to responses in a correct way.


Home / GST-Tally / How to File GSTR 3B Direct from TallyERP.9?
How to File GSTR 3B Direct from TallyERP.9?
In Latest Release 6.3, Tally Solution provide to file GSTR 3B direct from your Data, in this case you do not need any other software like offline utility or GSTR 3B Excel utility. Tally create a compatible Json file to upload it on GST Portal.
Today we discuss about this feature available in TallyERP.9 Release 6.3.
To create Json file go to - Gateway of Tally - Statutory Report - GST - GSTR 3B. Before proceed you need to Rectify your GSTR 3B queries. After resolve all the queries click on Export Return option which is available on right side bar.
There is two options: 1st option is Print return option, This option is for convert your GSTR 3B in Word Format, but you cannot save the return in word format so Click on Save as and save it in word format for your records.
2nd Option is of Export Return. Click on Export Return and export option will appear on your screen. Select Json file and accept.
If you select Excel option then you need Offline Utility to convert your file to Json format. This process take time, to save your time select JSON Format.
After select Json format Tally export your data in Json Format like this name "GSTR-3B_07AxxxPG5621P1ZV_November_2017-18.json".
Now Login to your GST Account and Select Return option, Select GSTR 3B - Prepare Offline. Click on browse and select your GSTR 3B File generated by Tally.


My project works fine in debug mode, except when i do a mdoify on the "main" function.
in summary, the total project is same except that there is a little change in "main" function, then in debug mode the behaviour is different and debug not start in main" function.
Could you let us know more details about asr_at_commands(0);and //application_start();?
Also you can do a comparison of these two functions by yourself.


Gris's beautiful exterior hides a simple, at times confusing, platformer.
An artful puzzle platformer that'll stay with you long after its short running time.
A stunning visual achievement and a mediocre Metroidvania but despite that unevenness this is still an affecting interactive journey with real emotional resonance.
A week or so removed from playing Gris, I don't know what I'll remember it for, if at all. Gris feels like it almost belongs in a museum, with crowds marvelling at its art and sound for a few minutes, before moving on to something else. There's moments of beautiful brilliance in Gris, all of which is dragged down by a decidedly average platforming game.
You won't want the experience to end, but when it does, it will stick in your head due its poignant atmosphere and incredible visuals. It's just a shame that it only lasted around three hours. The game's beauty and quality make it easy to not want it to end.
With GRIS, Nomada has created an abstract adventure, as beguiling as it is haunting. By focusing on its courageous protagonist, and creating a bewitching but frightening world for her to overcome, GRIS succeeds with its short but unforgettable odyssey. Awash with meticulous detail, charming aesthetics, and smart design, GRIS is something special. Take the trip.
GRIS is a true piece of art because it isn't only beautiful, but also meaningful. A game with excepcional design that, despite some minor flaws, will definitely get to your heart and make you feel. This is the closest to poetry that any videogame has ever come!
GRIS is a wonderful artistic achievement, with a simply sublime visual style and soundtrack that grows and evolves through the course of an emotional story.
GRIS is an evocative experience and from now on, an completely must have. It's an essential video game like titles such Journey or Limbo. It shows that videogames are art through audiovisual elements, as a spectacular artistic environment or a perfect game design. This game make us feel indescribable feelings.
It looks great, it's smooth, and you sure can jump a lot – but it's unsatisfying in so many ways that make it difficult to recommend over more competent and feature-rich games in the genre. After all, you know the old adage: beauty is only skin-deep.
Other games may have it beat in terms of pure gameplay, but GRIS is in a league of its own as a gaming experience.


text:
\section{Introduction}
Humans often perceive the world through multiple sensory modalities, such as watching, listening, smelling, touching, and tasting.
Consider two people sitting in a restaurant; seeing them across the table suggests that they maybe friends or coincidental companions; hearing, even the coarse demeanor of their conversation, makes the nature of their relationship much clearer.
In our daily life, there are many other examples that produce strong evidence that multi-modal co-occurrences give us fuller perception of events.
Recall how difficult it is to perceive the intricacies of the story from a {\em silent} film.
Multi-modal perception has been widely studied in areas like psychology~\cite{davenport1973cross,vroomen2000sound}, neurology~\cite{stein1993merging}, and human computer interaction~\cite{tanveer2015unsupervised}.

In the computer vision community, however, the progress in learning representations from multiple modalities has been limited, especially for high-level perceptual tasks where such modalities (\eg, audio or sound) can play an integral role.
Recent works~\cite{owens2018audio,senocak2018learning} propose approaches for localizing audio in unconstrained videos (sound source localization) or utilize sound in video captioning~\cite{hao2018integrating, hori2017attention, wang2018watch, tian2018attempt}.
However, these approaches consider relatively short videos, \ie, usually about 20 seconds, and focus on description of a single salient event~\cite{xu2016msr}.
More importantly, while they show that audio can boost the performance of visual models to an extent, such improvements are typically considered marginal and the role of audio is delegated to being secondary (or not nearly as important) as visual signal~\cite{hori2017attention,wang2018watch}.

\begin{figure}
  \centering
  \includegraphics[scale=0.50]{intro.pdf}
\caption{
{\bf Multi-modal Dense Event Captioning.}  Illustration of our problem definition, where we use both audio features and visual information to generate the dense captions for a video in a weakly supervised manner.}
\label{fig1:intro}
\vspace{-0.25in}
\end{figure}

We posit that sound (or audio) may in fact be much more important than the community may realize. Consider the previously mentioned example of a silent film. The lack of sound makes it significantly more difficult, if not impossible in many cases, to describe the rich flow of the story and constituent events. Armed with this intuition, we focus on dense event captioning \cite{li2018jointly,wang2018bidirectional,zhou2018end} ({\em a.k.a.} dense-captioning of events in videos~\cite{krishna2017dense}) and endow our models with ability to utilize rich auditory signals for both event localization and captioning. Figure~\ref{fig1:intro} illustrates one example of our multi-modal dense event captioning task.
Compared with conventional video captioning, dense event captioning deals with longer and more complex video sequences, usually 2 minutes or more.
To the best of our knowledge, our work is the first to tackle the dense event captioning with sound, treating sound as a first class perceptual modality.

Audio features can be represented in many different ways. Choosing the most appropriate representation for our task is challenging. To this end, we compare different audio feature representations in this work.
Importantly, we show that audio signal alone can achieve impressive performance on the dense event captioning task (rivalling visual counterpart).
The form of fusion needed to incorporate the audio with the video signal is another challenge.
We consider and compare a variety of fusion strategies.

Dense event captioning provides detailed descriptions for videos, which is beneficial for in-depth video analysis. However, training a fully supervised model requires both caption annotations and corresponding temporal segment coordinates (\ie, the start and end time of each event), which is extremely difficult and time consuming to collect. Recently, \cite{duan2018weakly} proposes a method for dense event captioning in a weakly supervised setting. The approach does not require temporal segment annotation during training. During evaluation, the model is able to detect all events of interest and generate their corresponding captions. Inspired by and building on~\cite{duan2018weakly}, we tackle our multi-modal dense event captioning in a weakly supervised manner.

\vspace{0.05in}
\noindent
{\bf Contributions.} Our contributions are multiple fold. First, to the best of our knowledge, this is the first work that addresses dense event captioning task in a multi-modal setting. In doing so, we propose an attention-based multi-modal fusion model to integrate both audio and video information. Second, we compare different audio feature extraction techniques~\cite{aytar2016soundnet, davis1980comparison, lidy2016cqt}, and analyze their suitability for the task.
Third, we discuss and test different fusion strategies for incorporating audio cues with visual features. Finally, extensive experiments on the ActivityNet Captions dataset~\cite{krishna2017dense} show that audio model, on its own, can nearly rival performance of a visual model and, combined with video, using our multi-modal weakly-supervised approach, can improve on the state-of-the-art performance.

\section{Related Work}

\noindent
{\bf Audio Feature Representations.}
Recently computer vision community has begun to explore audio features for learning good representations in unconstrained videos. Aytar \etal ~\cite{aytar2016soundnet} propose a sound network guided by a visual teacher to learn the representations for sound.
Earlier works, \cite{owens2018audio, senocak2018learning, sun2018indoor}, address sound source localization problem to identify which pixels or regions are responsible for generating a specified sound in videos (sound grounding). For example, \cite{senocak2018learning}~introduces an attention based localization network guided by sound information. A joint representation between audio and visual networks is presented in~\cite{owens2018audio, sun2018indoor} to localize sound source. Gao \etal~\cite{gao2018learning} formulate a new problem of audio source separation using a multi-instance multi-label learning framework. This framework maps audio bases, extracted by non-negative matrix factorization (NMF), to the detected visual objects. In recent year, audio event detection (AED)~\cite{cakir2015polyphonic, parascandolo2016recurrent, takahashi2016deep} has received attention in the research community. Most of the AED methods locate audio events and then classify each event.

\vspace{0.05in}
\noindent
{\bf Multi-modal Features in Video Analysis.}
Combining audio with visual features (\ie, multi-modal representation) often boosts performance of networks in vision, especially in video analysis ~\cite{arabaci2018multi, ariav2019end,hori2017attention,tian2018attempt,wang2018watch}. Ariav \etal~\cite{ariav2019end} propose an end-to-end deep neural network to detect voice activity by incorporating audio and visual modalities. Features from both modalities are fused using multi-modal compact bilinear pooling (MCB) to generate a joint representation for speech signal.
Authors in~\cite{arabaci2018multi} propose a multi-modal method for egocentric activity recognition where audio-visual features are combined with multi-kernel learning and boosting.

Recently, multi-modal approaches are also gaining popularity for video captioning~\cite{tian2018attempt,wang2018watch}. In~\cite{hori2017attention} a multi-modal attention mechanism to fuse information across different modalities is proposed.
Hori~\etal~\cite{hori2017early} extend the work in~\cite{hori2017attention} by applying hypothesis-level integration based on minimum Bayes-risk decoding~\cite{kumar2004minimum,stolcke1997explicit} to improve the caption quality.
Hao \etal~\cite{hao2018integrating} present multi-modal feature fusion strategies to maximize the benefits of visual-audio resonance information.
Wang \etal~\cite{wang2018watch} introduce a hierarchical encoder-decoder network to adaptively learn the attentive representations of multiple modalities, and fuse both global and local contexts of each modality for video understanding and sentence generation. A
module for exploring modality selection during sentence generation is proposed in~\cite{tian2018attempt} with the aim to interpret how words in the generated sentences are associated with audio and visual modalities.

\vspace{0.05in}
\noindent
{\bf Dense Event Captioning in Videos.}
The task of dense event captioning in videos was first introduced in~\cite{krishna2017dense}. The task involves detecting multiple events that occur in a video and describing each event using natural language.
Most of the works~\cite{liu2018best,yao2017msr} solve this problem in a two-stage manner, \ie, first temporal event proposal generation and then sentence captioning for each of the proposed event segments. In~\cite{yao2017msr}, authors adopt a temporal action proposal network to localize proposals of interest in videos, and then generate descriptions for each proposal.
Wang \etal~\cite{wang2018bidirectional} present a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions.
In~\cite{zhou2018end}, a differentiable masking scheme is used to ensure the consistency between proposal and captioning modules. Li \etal~\cite{li2018jointly} propose a descriptiveness regression component to unify the event localization and sentence generation.
Xu \etal~\cite{xu2019joint} present an end-to-end joint event detection and description network (JEDDi-Net) which adopts region convolutional 3D network~\cite{xu2017r} for proposal generation and refinement, and proposes hierarchical captioning.

Duan \etal~\cite{duan2018weakly} formulate the dense event captioning task in a weakly supervised setting, where there is no ground-truth temporal segment annotations during training and evaluation. They decompose the task into a pair of dual problems, event captioning and sentence localization, and present an iterative approach for training. Our work is motivated by~\cite{duan2018weakly} and builds on their framework. However, importantly, we fuse audio and visual features, and explore a variety of fusion mechanisms to address the multi-modal weakly supervised dense event captioning task. We note that~\cite{duan2018weakly} is thus far the only method for dense event captioning in the weakly supervised setting.
\vspace{-0.10in}
\section{Multi-modal Dense Event Captioning}
In this work, we consider two important modalities, audio and video, to generate dense captions in a weakly supervised setting. Weak supervision means that we do not require ground-truth temporal event segments during training. The overview of our multi-modal architecture is shown in Figure~\ref{fig:block_diagram}. The architecture consists of two modules, a {\em sentence localizer} and a {\em caption generator}.
Given a set of initial random proposal segments in a video, caption generator produces captions for the specified segments. Sentence localizer then refines the corresponding segments with the generated captions. Caption generator is employed again to refine the captions. This process can proceed iteratively to arrive at consistent segments and captions; in practice we use one iteration following the observations in~\cite{duan2018weakly}.

\begin{figure*}[ht]
  \centering
  \includegraphics[scale=0.70]{block_diagram.pdf}
\caption{
{\bf Our Multi-modal Architecture.} The model has two parts, a {\em sentence localizer} and a {\em caption generator}. The {\em sentence localizer} takes audio, video, and captions as inputs and generates a temporal segment for each caption. The {\em caption generator} uses the resultant temporal segments, with audio and video features, to produce a caption for each segment.}
\label{fig:block_diagram}
\vspace{-0.22in}
\end{figure*}

We extract features from audio, video, and captions first, and pass them as inputs to the sentence localizer during training. For each modality, an encoder is used to encode the input. We use recurrent neural networks (RNNs) with GRU~\cite{cho2014learning} units as encoders. We then apply a \textit{crossing attention} among the audio, video and caption features. Then an \textit{attention feature fusion} mechanism followed by a fully-connected layer is applied to produce temporal segments.

The caption generator takes the encoded features of audio and video, along with the resultant temporal segments as inputs. It performs~\textit{soft mask clipping} on the audio and video features based on the temporal segments, and uses a \textit{context fusion} technique to generate the multi-modal context features. Then a caption decoder, which is also an RNN with GRU units, generates one caption for each multi-modal context feature. We discuss and compare three different context fusion strategies to find the most appropriate one for our multi-modal integration.

In what follows, we first describe how to extract features from audio and video in Sec.~\ref{Feature_Representation}. Then we present our weakly supervised approach in Sec.~\ref{wsdec}. Lastly, we demonstrate three different context fusion strategies in section~\ref{cf}.

\subsection{Feature Representation}\label{Feature_Representation}
We consider both features from audio and video modalities for dense event captioning. It is generally challenging to select the most appropriate feature extraction process, especially for the audio modality. We describe different feature extraction methods to process both audio and video inputs.

\vspace{-0.20in}

\subsubsection{Audio Feature Processing} \label{sec:audio_feature}
ActivityNet Captions dataset~\cite{krishna2017dense} does not provide audio tracks. As such, we collected all audio data from the YouTube videos via the original URLs. Some videos are no longer available on YouTube. In total, we were able to collect around 15,840 audio tracks corresponding to ActivityNet videos. To process the audio, we consider and compare three different audio feature representations.

\vspace{0.05in}
\noindent
\textbf{MFCC Features.} Mel-Frequency  Cepstrum (MFC) is a common representation for sound in digital signal processing. Mel-Frequency Cepstral Coefficients (MFCCs) are coefficients that collectively make up an MFC -- a representation of the short-term power spectrum of sound~\cite{jiang2009music}. We down-sample the audio from 44 kHz to 16 kHz and use 25 as the sampling rate. We choose 128
MFCC features,
with 2048 as the FFT window size and 512 as the number of samples between successive frames (\ie, hop length).

\vspace{0.05in}
\noindent
\textbf{CQT Features.} The Constant-Q-Transform (CQT) is a time-frequency representation where the frequency bins are geometrically spaced and the ratios of the center frequencies to bandwidths (Q-factors) of all bins are equal~\cite{brown1991calculation}. CQT is motivated from the human auditory system and the fundamental frequencies of the tones in Western music~\cite{schorkhuber2010constant}. We perform feature extraction by choosing 64 Hz and 60 as the minimum frequency and the number of frequency bins respectively. Similar to the MFCC features described above, we use 2048 as the FFT window size and 512 as the hop length. We use VGG-16~\cite{simonyan2014very} without the last classification layer to convert both MFCC and CQT features into 512-dimensional representations.

\vspace{0.05in}
\noindent
\textbf{SoundNet Features.} SoundNet~\cite{aytar2016soundnet} is a CNN that learns to represent raw audio waveforms. The acoustic representation is learned using two-million videos with their accompanying audios; leveraging the natural synchronization between them. We use a pretrained SoundNet~\cite{aytar2016soundnet} model to extract the 1000-dimension audio features from the 8-th convolutional layer (\ie, conv8) for each video's audio track.

\vspace{-0.10in}

\subsubsection{Video Feature Processing}\label{sec:video_feature}
Given an input video $\mathbf{V}=\{{\mathbf{v}_t\}^{T_v}_{t=1}}$, where $\mathbf{v}_t$ is the video frame at time $t$ and $T_v$ is the video length, a 3D-CNN model is used to process the input video frames into a sequence of visual features $\{\mathbf{f}_t = F(\mathbf{v}_t:\mathbf{v}_{t+\delta})\}_{t=1}^{T_{f}}$. Here, $\delta$ means the time resolution for each feature $\mathbf{f}_t$ and $T_f$ is the length of the feature sequence. We use features extracted from encoder $F$ provided by the ActivityNet Captions dataset~\cite{krishna2017dense}, where $F$ is the pretrained C3D~\cite{ji20133d} network with $\delta=16$ frames. The dimension of the resultant C3D features is a tensor of size $T_f \times D$, where $D=500$ and $T_f=T_v/\delta$.

\begin{figure*}[ht]
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[scale=.52]{fusion1.pdf}
  \caption{Multiplicative mixture fusion}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[scale=0.52]{fusion2.pdf}
  \caption{Multi-model context fusion}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[scale=0.52]{fusion3.pdf}
  \caption{MUTAN fusion}
\end{subfigure}
\vspace{-0.10in}
\caption{{\bf Context Fusion Strategies.} Three fusion strategies are illustrated: (a) multiplicative mixture fusion, (b) multi-modal context fusion, and (c) MUTAN fusion.}
\label{fg:fusion}
\vspace{-0.22in}
\end{figure*}
\subsection{Weakly Supervised Model}\label{wsdec}
Weak supervision means that we do not require ground-truth temporal alignments between the video (visual and audio collectively) and captions. We make a one-to-one correspondence assumption, meaning that we assume that each caption describes one temporal segment and each temporal segment corresponds to only one caption. This assumption holds in the current benchmark dataset and most real world scenarios. We employ two network modules, a sentence localizer and a caption generator. Given a caption, the sentence localizer will produce a temporal segment in the \textit{context}, while the caption generator will generate a caption with a given temporal segment. We use \textit{context} to refer an encoded video or audio.

\vspace{0.05in}
\noindent
\textbf{Notations.}
We use GRU RNNs to encode visual and audio streams of the video. This results in a sequence of output feature vectors, one per frame, $\mathbf{O} = \{\mathbf{o}_t \in \mathbb{R}^k\}_{t=0}^{T_o}$ and the final hidden state $\mathbf{h}^{o} \in \mathbb{R}^k$, where $T_o$ is the length of the video. While in practice we get two sets of such vectors (one set for video and one set for corresponding audio ``frames"), we omit the subscript for clarity of formulation that follows. A caption is encoded similarly by the output features of the RNN, $\mathbf{C} = \{ \mathbf{c}_t \in \mathbb{R}^k\}_{t=0}^{T_c}$ with the last hidden state being $\mathbf{h}^{c} \in \mathbb{R}^k$, where $T_c$ is the length of the caption in words. We use {\em context} to refer the encoding of the full visual or audio information in videos.
A context segment $\mathbf{S}$ is represented by $(c, l)$, where $c$ and $l$ denote segment's temporal center and length respectively within $\mathbf{O}$.

\vspace{-0.15in}
\subsubsection{Sentence Localizer}\label{sec:senlocal}
Sentence localizer attempts to localize a given caption in a video by considering the caption and the encoded complete video ({\em context}). Formally, given a (video or audio) context $\mathbf{O}$ and an encoded caption $\mathbf{C}$, sentence localizer will regress a temporal segment $\mathbf{S}$ in $\mathbf{O}$. With the context and caption features, it first applies \textit{crossing attention} among them. Then an \textit{attention feature fusion}, followed by one layer fully-connected neural network, is used to generate the temporal segment. Following ~\cite{duan2018weakly}, we use 15 predefined temporal segments and generate 15 offsets in sentence localization using fully connected layer. The final segments are the sum of temporal segments and offsets value. The purpose is to fine-tune the offset value for best localization.

\vspace{0.05in}
\noindent
\textbf{Crossing Attention.} The crossing attention consists of two sub-attentions, one caption attention $\mathbf{Att}_\mathrm{c}$, and one context attention $\mathbf{Att}_\mathrm{o}$. For a context $\mathbf{O}$ and a caption $\mathbf{C}$, we first compute the attention between $\mathbf{h}^{o}$ and $\mathbf{C}$ as:
\begin{equation} \label{eq:att_c}
    \mathbf{Att}_\mathrm{c} = \operatorname{softmax}((\mathbf{h}^o)^T\bm{\alpha}_c\mathbf{C})\mathbf{C}^T,
\end{equation}
and then calculate the attention between $\mathbf{h}^{c}$ and $\mathbf{O}$ as:
\begin{equation} \label{eq:att_o}
    \mathbf{Att}_\mathrm{o} = \operatorname{softmax}((\mathbf{h}^c)^T\bm{\alpha}_o\mathbf{O})\mathbf{O}^T,
\end{equation}
where $\bm{\alpha}_c \in \mathbb{R}^{k \times k}$ and $\bm{\alpha}_o \in \mathbb{R}^{k \times k}$ are the learnable attention weights, and $()^T$ is the matrix transpose operation. We note that $\mathbf{Att}_\mathrm{o}$ is a vector of size $1 \times k$ comprising of attention weighted features for the visual/audio frames; similarly $\mathbf{Att}_\mathrm{c}$ is a vector of size $1 \times k$ of attended caption features.

When training our multi-modal approaches, the caption attention $\mathbf{Att}_\mathrm{c}$ is calculated only between the visual modality and the captions, and we generate video attention $\mathbf{Att}_\mathrm{v}$ and audio attention $\mathbf{Att}_\mathrm{a}$ using Eq.~\ref{eq:att_o}. While we are training our unimodal approaches which either use audio (or video) information to generate captions, the caption attention $\mathbf{Att}_\mathrm{c}$ is calculated between the audio (or video) and captions.

\vspace{0.05in}
\noindent
\textbf{Attention Feature Fusion.} After obtaining the sub-attentions, we use the multi-model feature fusion technique~\cite{gao2017tall} to fuse them together:
\begin{align}
    \mathbf{Att}_{\mathrm{sum}} &= \mathbf{Att}_\mathrm{c} + \mathbf{Att}_\mathrm{v} + \mathbf{Att}_\mathrm{a} \\
    \mathbf{Att}_{\mathrm{dot}} &= \mathbf{Att}_\mathrm{c} \cdot \mathbf{Att}_\mathrm{v} \cdot \mathbf{Att}_\mathrm{a} \\
    \mathbf{Att}_{\mathrm{fc}} &= \operatorname{fc}(\mathbf{Att}_\mathrm{c} || \mathbf{Att}_\mathrm{v} || \mathbf{Att}_\mathrm{a}) \\
    \mathbf{Att}_{\mathrm{fusion}} &= \mathbf{Att}_{\mathrm{sum}}||\mathbf{Att}_{\mathrm{dot}}||\mathbf{Att}_{\mathrm{fc}} \label{feature_fusion}
\end{align}
where $+$ and $\cdot$ are the element-wise addition and multiplication, $||$ is the column-wise concatenation, and $\operatorname{fc}(\cdot)$ is a one-layer fully-connected neural network.

\subsubsection{Caption Generator}
Given a temporal segment $\mathbf{S}$ in a context $\mathbf{O}$, the caption generator will generate a caption based on $\mathbf{S}$. With the temporal segments generated by the sentence localizer (Sec.~\ref{sec:senlocal}), the caption generator first applies \textit{soft mask clipping} on the contexts, and then uses a \textit{context fusion} mechanism (Sec.~\ref{cf}) to fuse the clipped contexts together. The fused contexts are then fed to a caption decoder, which is also a GRU RNN, to generate the corresponding captions.

\vspace{0.05in}
\noindent
\textbf{Soft Mask Clipping.} Getting a temporal segment $\mathbf{S}$ from a context, \ie, the clipping operation, is non-differentiable, which makes it difficult to handle in end-to-end training. To this end, we utilize a continuous mask function with regard to the time step $t$ to perform soft clipping. The mask $\operatorname{M}$ to obtain an $\mathbf{S}$ is defined as follows:
\begin{equation}
    \operatorname{M}(t, \mathbf{S}) = \operatorname{\sigma}(-L(t-c+\frac{l}{2})) - \operatorname{\sigma}(-L(t-c-\frac{l}{2})),
\end{equation}
where $\operatorname{\sigma}(\cdot)$ is the sigmoid function, and $L$ is a scaling factor. When $L$ is large enough, this mask function becomes a step function which performs the exact clipping. We use the normalized weighted sum of the context features (weighted by the mask) as a feature representing $\mathbf{S}$. This operation approximates traditional mean-pooling over clipped frames.

\subsection{Context Fusion}\label{cf}
Because audio and visual representations are from two different modalities, merging them together is a crucial task in a multi-modal setting. We use three different context merging techniques (Fig.~\ref{fg:fusion}) to fuse the video $\mathbf{V}^\prime$ and audio $\mathbf{A}^\prime$ features obtained after the normalized soft mask clipping operation. We treat $\mathbf{V}^{\prime}$ and $\mathbf{A}^\prime$ as row vectors.

\vspace{0.05in}
\noindent
\textbf{Multiplicative Mixture Fusion.} The multiplicative mixture fusion can make the model automatically focus on information from a more reliable modality and reduce emphasis on the less reliable one~\cite{liu2018learn}. Given a pair of features $\mathbf{V}^{\prime}$ and $\mathbf{A}^\prime$, the multiplicative mixture fusion first adds these two contexts and then concatenates the added context with the two original ones. That is, it produces a final context as follows,
\begin{equation}
    \mathbf{C}_{\mathrm{final}} = (\mathbf{V}^{\prime} + \mathbf{A}^\prime) || \mathbf{V}^{\prime} || \mathbf{A}^\prime
\end{equation}
where $+$ and $||$ are the element-wise addition and column-wise concatenation respectively.

\vspace{0.05in}
\noindent
\textbf{Multi-modal Context Fusion.} This fusion strategy is similar to Eq.~\ref{feature_fusion}. But here, we apply the fusion technique on $\mathbf{A}^\prime$ and $\mathbf{V}^{\prime}$ (segments as opposed to full video context),

\begin{equation}
    \mathbf{C}_{\mathrm{final}} = (\mathbf{V}^{\prime} + \mathbf{A}^\prime)||(\mathbf{V}^{\prime} \cdot \mathbf{A}^\prime)||\operatorname{fc}(\mathbf{V}^{\prime} || \mathbf{A}^\prime).
\end{equation}

\vspace{0.05in}
\noindent
\textbf{MUTAN Fusion.} MUTAN fusion was first proposed in~\cite{ben2017mutan} to solve visual question answering tasks by fusing visual and linguistic features. We adopt the fusion scheme to fuse $\mathbf{V}^{\prime}$ and $\mathbf{A}^\prime$. With the idea of Tucker decomposition~\cite{tucker1966some}, we first reduce the dimension of $\mathbf{V}^{\prime}$ and $\mathbf{A}^\prime$,
\begin{align}
    \mathbf{V}'' &= \operatorname{tanh}(\mathbf{V}^{\prime} \times \mathbf{W}_v) \\
    \mathbf{A}'' &= \operatorname{tanh}(\mathbf{A}^{\prime} \times \mathbf{W}_a)
\end{align}
where $\mathbf{W}_v$ and $\mathbf{W}_a$ are learnable parameters and $\operatorname{tanh}(\cdot)$ is the hyperbolic tangent function. Then we produce final context as folows:
\begin{align}
     \tilde{\mathbf{C}} &= ((\mathbf{T}_c \times_1 \mathbf{V}'') \times_2 \mathbf{A}'') \\
     \mathbf{C}_{\mathrm{final}} &=   \operatorname{squeeze}(\tilde{\mathbf{C}}) \times \mathbf{W}_o,

\end{align}
where $\mathbf{T}_c$ and $\mathbf{W}_o$ are learnable parameters. $\times_i, i \in \{1, 2\}$ denotes the mode-$i$ product between a tensor and a matrix, and $\times$ is the matrix multiplication operation. $\mathbf{T}_c$ models the interactions between the video and the audio modalities, which is a 3-dimension tensor; $\operatorname{squeeze}$ operator squeezes $\mathbf{\tilde{C}}$ into a row vector.

\subsection{Training Loss}
We follow the training procedure and loss function presented in~\cite{duan2018weakly} to train our networks. We employ the idea of cycle consistency~\cite{zhu2017unpaired} to train the sentence localizer and the caption generator, and treat the temporal segment regression as a classification problem. The final training loss is formulated as
\begin{equation}\label{eq:loss}
    L =  L_{\mathrm{c}} + \lambda_{s}L_{\mathrm{s}} + \lambda_{r}L_{\mathrm{r}}
\end{equation}
where $\lambda_{s}$ and $\lambda_{r}$ are tunable hyperpramaters. $L_{\mathrm{c}}$ is the caption reconstruction loss, which is a cross-entropy loss measuring the similarity between two sentences. $L_{\mathrm{s}}$ is the segment reconstruction loss, which is an L2 loss. It measures the similarity between two temporal segments. $L_{\mathrm{r}}$ is the temporal segment regression loss, which is also a cross-entropy loss, because we regard the temporal segment regression as a classification problem.

\section{Experiments}
In this section, we first describe the dataset used in our experiments, which is an extension of the ActivityNet Captions Dataset~\cite{krishna2017dense} (Sec.~\ref{sec:dataset}). Then we present the experimental setup and implementation details (Sec.~\ref{sec:setup}). Lastly, we discuss the experimental results for both unimodal (\ie, trained using either audio or video modality) and multi-modal approaches (Sec.~\ref{sec:results}).

\subsection{Dataset}\label{sec:dataset}
ActivityNet Captions dataset~\cite{krishna2017dense} is a benchmark for large-scale dense event captioning in videos. The dataset consists of 20,000 videos where each video is annotated with a series of temporally aligned captions. On average, one video corresponds to 3.65 captions. However, besides the captions, the current dataset only provides C3D features~\cite{ji20133d} for visual frames, no original videos. To obtain the audio tracks for those videos, we needed to find the original videos on YouTube and download the audios via the provided URLs. Around 5,000 videos are unavailable on YouTube now. We are able to find 8026 videos (out of 10009 videos) for training and 3880 videos (out of 4917 videos) for validation. We use those available training/validation videos throughout our experiments.

\subsection{Experiment Setup and Implementation Details}\label{sec:setup}
We follow the experiment protocol in~\cite{duan2018weakly} to train and evaluate all the models. We consider the models proposed in~\cite{duan2018weakly} as our baselines, \ie, unimodal models that only utilize audio or visual features. Due to the difference in the number of videos for training and validation from the original dataset, we run all the experiments from scratch using the PyTorch implementation provided by~\cite{duan2018weakly}\footnote{\url{https://github.com/XgDuan/WSDEC}}. The dimensions of the hidden and output layers for all GRU RNNs (audio/video/caption encoders and caption decoders) are set to 512. We also follow~\cite{duan2018weakly} to build the word vocabulary (containing 6,000 words) and preprocess the words.

\begin{table}
\scriptsize
   \begin{center}
  \begin{tabular}{lcccccccc}
  \hline
    Features & M & C & R & B@1 & B@2 & B@3 & B@4 & S \\
    \hline \hline
    \multicolumn{8}{l}{\textbf{Pretrained model}} \\
    MFCC & 2.70 & 6.46 & 6.74 & 5.52 & 1.74 & 0.67 & 0.21 & 3.51\\
    CQT      & 2.38 & 5.60 & 5.72 & 4.37 & 1.57 & 0.46 & 0.13 & 2.90\\
    SoundNet & 2.63 & 5.76 & 6.99 & 6.28 & 1.81 & 0.38 & 0.12 & 3.44\\
    \hline
     \multicolumn{8}{l}{\textbf{Final model}} \\
    MFCC & 3.36 & 9.56 & 8.51 & 6.68 & 2.55 & 1.23 & 0.60 & 4.20\\
    CQT & 3.25 & 8.97 & 7.43 & 6.34 & 2.69 & 0.93 & 0.32 & 3.63\\
    SoundNet & 3.41 & 9.21 & 8.50 & 7.19 & 2.15 & 0.49 & 0.13 & 4.22 \\
    \hline
  \end{tabular}
  \end{center}
  \vspace{-0.15in}
  \caption{{\bf Audio Only Results.} Illustrated are dense captioning results of pretrained and final models using audio only.}
  \label{table:pretrain_cg_audio}
  \vspace{-0.22in}
\end{table}

\begin{table*}
\footnotesize
   \begin{center}
  \begin{tabular}{lccccccccc}
  \hline
    Fusion Strategies & M & C & R & B@1 & B@2 & B@3 & B@4 & S & mIoU\\
    \hline \hline
    \multicolumn{8}{l}{\textbf{Pretrained model}} \\
    Multiplicative mixture fusion & 3.59 & 8.12 & 7.51 & 7.12 & 2.74 & 1.22 & 0.56 & 4.58 & -\\
    Multi-modal context fusion & 3.55 & 7.91 & 7.54 & 7.24 & 2.78 & 1.28 & 0.62 & 4.45 & -\\
    MUTAN fusion & \textbf{3.71} & \textbf{8.20} & \textbf{7.71} & \textbf{7.45} & \textbf{2.92} & \textbf{1.31} & \textbf{0.63} & \textbf{4.78} & -\\
    \hline
     \multicolumn{8}{l}{\textbf{Final model}} \\
     Multiplicative mixture fusion & 4.89 & \textbf{13.97} & 10.39 & 9.92 & 4.17 & 1.85 & 0.88 & 5.95 & 29.87\\
     Multi-modal context fusion & \textbf{4.94} & 13.90 & 10.37 & 9.95 & 4.20 & \textbf{1.86} & 0.89 & 5.98 & 29.91\\
     MUTAN fusion & 4.93 & 13.79 & \textbf{10.39} & \textbf{10.00} & \textbf{4.20} & 1.85 & \textbf{0.90} & \textbf{6.01} & \textbf{30.02}\\
    \hline
  \end{tabular}
  \end{center}
  \vspace{-0.20in}
  \caption{{\bf Fusion Strategies.} Testing results for different context fusion strategies for integrating audio and video modalities are illustrated for both pretrained and final models. We use MFCC audio features and C3D video features for all experiments.}
  \label{table:fusion_comparison}
\end{table*}

\begin{table*}
\footnotesize
   \begin{center}
  \begin{tabular}{lcccccccccc}
  \hline
    Model & M & C & R & B@1 & B@2 & B@3 & B@4 & S & mIoU \\
    \hline \hline
    \multicolumn{8}{l}{\textbf{Pretrained model}} \\
    Unimodal (C3D video feature)~\cite{duan2018weakly} & 3.66 & 8.20 & 7.42 & 7.06 & 2.76 & 1.29 & 0.62 & 4.41 & - \\
    Unimodal (SoundNet audio feature) & 2.63 & 5.76 & 6.99 & 6.28 & 1.81 & 0.38 & 0.12 & 3.44 & - \\
    Unimodal (MFCC audio feature) & 2.70 & 6.46 & 6.74 & 5.52 & 1.74 & 0.67 & 0.21 & 3.51 & - \\
    \hline
    Multi-modal (SoundNet audio + C3D video feature) & 3.72 & 8.02 & 7.50 & 7.12 & 2.74 & 1.23 & 0.58 & 4.46 & - \\
    Multi-modal (MFCC audio + C3D video feature) & 3.71 & 8.20 & 7.71 & 7.45 & 2.92 & 1.31 & 0.63 & 4.78 & - \\% & -\\
    \hline
    \multicolumn{8}{l}{\textbf{Final model}} \\
    Unimodal (C3D video feature)~\cite{duan2018weakly} & 4.89 & 13.81 & 9.92 & 9.45 & 3.97 & 1.75 & 0.83 & 5.83 & 29.78 \\
    Unimodal (SoundNet audio feature) & 3.41 & 9.21 & 8.50 & 7.19 & 2.15 & 0.49 & 0.13 & 4.22 & 25.57 \\
    Unimodal (MFCC audio feature) & 3.36 & 9.56 & 8.51 & 6.68 & 2.55 & 1.23 & 0.60 & 4.20 & 27.16 \\
    \hline

    Multi-modal (SoundNet audio + C3D video feature) & {\bf 5.03} & {\bf 14.27} & 10.35 & 9.75 & 4.19 & {\bf 1.92} & {\bf 0.94} & {\bf 6.04} & 29.96 \\
    Multi-modal (MFCC audio + C3D video feature) & 4.93 & 13.79 & {\bf 10.39} & {\bf 10.00} & {\bf 4.20} & 1.85 & 0.90 & 6.01 & {\bf 30.02} \\
    \hline
  \end{tabular}
  \end{center}
  \vspace{-0.20in}
  \caption{{\bf Multi-modal Results.} Comparison among unimodal and our multi-modal models using MUTAN fusion.}
  \label{table:multimodal_results}
  \vspace{-0.10in}
\end{table*}

\begin{table*}[h]
\vspace{-0.05in}
\footnotesize
   \begin{center}
  \begin{tabular}{lcccccccc}
  \hline
    Model & M & C & R & B@1 & B@2 & B@3 & B@4 & S\\
    \hline \hline
    Unimodal (C3D)~\cite{duan2018weakly} & 7.09 & 24.46 & 14.79 & 14.32 & 6.23 & 2.89 & 1.35 & 8.22\\
    Multi-modal (SoundNet audio feature + C3D video feature) & 7.02 & 24.22 & 14.66 & 14.18 & 6.13 & 2.88 & 1.41 & 7.89\\
    Multi-modal (MFCC audio feature + C3D video feature) & \textbf{7.23} & \textbf{25.36} & \textbf{15.37} & \textbf{15.23} & \textbf{6.58} & \textbf{3.04} & \textbf{1.46} & \textbf{8.51}\\
    \hline
  \end{tabular}
  \end{center}
  \vspace{-0.20in}
  \caption{Results with ground-truth temporal segments.}
  \label{table:gt_seg_results_rebuttal}
  \vspace{-0.05in}
\end{table*}

\begin{table*}[h]
\footnotesize
   \begin{center}
  \begin{tabular}{lcccccccc}
  \hline
    Model & M & C & R & B@1 & B@2 & B@3 & B@4 & S \\
    \hline \hline

    Unimodal (C3D)~\cite{duan2018weakly} & 4.58 & 10.45 & 9.27 & 8.7 & 3.39 & 1.50 & 0.69 & -\\
    Multi-modal (SoundNet + C3D) & 4.70 & 10.32 & 9.40 & 8.95 & 3.40 & 1.53 & 0.73 & 5.51\\
    Multi-modal (MFCC + C3D) & \textbf{4.78} & \textbf{10.53} & \textbf{9.60} & \textbf{9.23} & \textbf{3.62} & \textbf{1.69} & \textbf{0.82} & \textbf{5.56}\\
    \hline
  \end{tabular}
  \end{center}
  \vspace{-0.20in}
  \caption{Pretrained model results on the full dataset.}
  \label{table:multimodal_results_rebuttal}
  \vspace{-0.20in}
\end{table*}

\begin{figure*}[h!]
  \centering
  \includegraphics[scale=0.78]{qualatitive_result_new.pdf}
\caption{{\bf Qualitative Results.} Both pretrained and final model results are illustrated of two videos. Captions are from (a) ground-truth; (b) pretrained model trained only using visual features; (c) multi-modal pretrained model; (d) final model trained with video features only; (e) our multi-modal final model for dense event captioning in videos.}
\label{fig:qualatative_result}
\vspace{-0.20in}
\end{figure*}

\vspace{0.05in}
\noindent
\textbf{Training.} Weak supervision means that we do not have ground-truth temporal segments. We first train the caption generator only (\textit{pretrained model}), and then train the sentence localizer and caption generator together (\textit{final model}). To train the pretrained model, we input the entire context sequence (\textit{Fake Proposal}, $\mathbf{S}=(0.5, 1)$). We use the weights of the pretrained model to initialize the relevant weights in the final model.
For both pretrained model and final model, we train them in both unimodal and multi-modal settings.
To train unimodal models, we use initial learning rates 0.0001 and 0.01 for audio and video respectively with a cross-entropy loss. While training our multi-modal models, we set the initial learning rates to 0.0001 for the network parts that have been initialized with the pretrained weights, and 0.01 for other network components. $\lambda_s$ and $\lambda_r$ in Eq.~\ref{eq:loss} are both set to 0.1. We train the networks using stochastic gradient descent with a momentum factor of 0.8.

\vspace{0.05in}
\noindent
\textbf{Testing.}
To test the pretrained models, we select one random ground truth description as well as random temporal segment instead of entire video unlike training.
For the final models, following~\cite{duan2018weakly}, we start from 15 randomly guessed temporal segments, and apply one round of fixed-point iteration and the IoU filtering mechanism to obtain a set of filtered segments. Caption generators are applied to the filtered segments together with context features to produce the dense event captions.

\vspace{0.05in}
\noindent
\textbf{Evaluation metrics.} We measure the performance of captioning results using traditional evaluation metrics: METEOR (M)~\cite{banerjee2005meteor}, CIDEr (C)~\cite{vedantam2015cider}, Rouge-L (R)~\cite{lin2004automatic}, Spice (S)~\cite{anderson2016spice} and Bleu@N (B@N)~\cite{papineni2002bleu}. For score computations, we use official scripts provided by~\cite{krishna2017dense}\footnote{\url{https://github.com/ranjaykrishna/densevid_eval}}. Where appropriate, we use mean Intesection over Union (mIoU) to measure segment localization performance.

\subsection{Experiment Results}\label{sec:results}
Since audio features can be represented in a variety of ways~\cite{aytar2016soundnet, schorkhuber2010constant, vergin1999generalized}, finding the best representation is challenging. We conduct experiments on both pretrained models and final models using different audio representations, \ie, MFCC~\cite{jiang2009music}, CQT~\cite{brown1991calculation}, and SoundNet~\cite{aytar2016soundnet}, which are described in Sec.~\ref{sec:audio_feature}. Table~\ref{table:pretrain_cg_audio} shows the experiment results of pretrained models and final models using only audio features. We can see that both MFCC and soundNet can generate comparable results.

As discussed in Sec.~\ref{cf}, in the multi-modal setting, choosing a good fusion strategy to combine both audio and video features is another crucial point. Table~\ref{table:fusion_comparison} shows comparison of different context fusion techniques using MFCC audio representations and C3D visual features (Sec.~\ref{sec:video_feature}) for both pretrained models and final models. Among all fusion techniques, we find that MUTAN fusion is the most appropriate one for our weakly supervised multi-modal dense event captioning task. Therefore, we decide to use MUTAN fusion technique for our multi-modal models when comparing to unimodal models. Tab.~\ref{table:multimodal_results} shows the testing results for comparison among unimodal and multi-modal approaches. We can see that our multi-modal approach (both MFCC and SoundNet audio with C3D video features) outperforms state-of-the-art unimodal method~\cite{duan2018weakly} in most evaluation metrics. Specifically on the Bleu@3 and Bleu@4 scores, it leads to 9\% and 13\% improvement respectively. Comparing among unimodel approaches, we are surprised to find that only using audio features achieves competitive performance.
We trained our caption generator with GT segments to remove the effect of localization. The results are shown in Table~\ref{table:gt_seg_results_rebuttal}. We also conduct experiment on pretrain caption generator using the full dataset where for some videos, audio data is not available (treated as missing data). We consider zero feature vectors for missing audios. The results are shown in Table~\ref{table:multimodal_results_rebuttal}. In addition, we randomly selected 15 validation videos and invited 20 people to conduct human evaluation for comparing our multi-modal model to the visual-only one. The forced choice preference rate for our multi-modal model is 60.67\%.

Figure~\ref{fig:qualatative_result} demonstrates some qualitative results for both pretrained models and final models. It displays the ground-truth captions along with the ones generated by unimodal models and our multi-modal models. The arrow segments indicate the ground-truth or detected temporal event segments. We utilize C3D visual features along with audio features. We can see that our multi-modal approaches outperform unimodal ones, both on caption quality and temporal segment accuracy.

Similar to~\cite{duan2018weakly}, we are suffering from two limitations. One is that sometimes our multi-modal model can not detect the beginning of an event correctly. The other is that most of the time our final model only generates around 2 event captions, which means that the multi-modal approach is still not good enough to detect all the events in the weakly supervised setting. Overcoming of these two limitations is the focus of our future work.

\vspace{-0.20in}

\section{Conclusion}
\vspace{-0.08in}
Audio is a less explored modality in the computer vision community. In this paper, we propose a muti-modal approach for dense event captioning in a weakly supervised setting. We incorporate both audio features with visual ones to generate dense event captions for given videos. We discuss and compare different feature representation methods and context fusion strategies. Extensive experiments illustrate that audio features can play a vital role, and combining both audio and visual modalities can achieve performance better than the state-of-the-art unimodal visual model.

\vspace{0.05in}
\noindent
\textbf{Acknowledgments:} This work was funded in part by the Vector Institute for AI, Canada CIFAR AI Chair, NSERC Canada Research Chair (CRC) and an NSERC Discovery and Discovery Accelerator Supplement Grants.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
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Ranking the health of nearly every county in the nation, County Health Rankings illustrate what is known when it comes to what's making people sick or healthy. County Health Roadmaps show what can be done to create healthier places to live, learn, work and play.
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Free resource for health care professionals interested in tracking performance on various measures of health care quality. Enables organizations to compare performance against that of peer organizations, against a range of benchmarks, and over time. Case studies and improvement tools spotlight successful improvement strategies of the nation's top performers. Regional maps shows performance at the county, hospital referral region, state, and national levels.
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Christ's Atonement is stronger than storms on the sea--and it's Jesus's gift to you and to me. This reverent, rhyming picture book teaches that the Atonement can help us overcome any challenge we face, whether we've made a bad choice or feel lonely or scared. Help your children learn that when we turn to Jesus Christ, we can do hard things and become our best selves.
Sierra Wilson started talking early and has been a storyteller ever since. She loves to teach and work with children, and this love has shaped her life from working as a junior high/high school English teacher and children's art teacher to serving as a church children's leader and a mother. Sierra is passionate about writing, art, hiking, family time, and eating entirely too much chocolate. She currently lives with her husband and three young children in the prairies of Alberta, where she is a toddler-chaser by day and a writer/artist by night.
The Atonement of Jesus Christ is for Me by Sierra Wilson and illustrated by Corey Egbert is a fabulous children's book and teaching tool for parents and church leaders. Filled with delightfully bright and colorful pictures that coincide with the story and explination of Jesus Christ and the Atonement are expressed in a way that children will enjoy, discover, and learn. The Atonement is a hard topic to help children understand and this book is written and illustrated in a way that children can understand. This is an excellent resource and tool for teaching young children.
A review copy of this book was provided by the author via the publisher, Cedar Fort. A review was not required and all views and opinions expressed are my own.


text:
Lace Up for the UNCF Alabama Virtual Walk for Education
Mary K Published: August 30, 2021
Rupert Weidemann
The United Negro College Fund (UNCF) is one of the best organizations benefiting minority education. The UNCF was founded in 1944, and they have raised more than $4.8 billion that assists economically disadvantaged scholars to complete their post-secondary education.
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The UNCF "supports students' education and development through scholarships and other programs, strengthening its 37 member colleges and universities. UNCF advocates for the importance of minority education and college readiness."
Even though the pandemic is still happening, you can still support the UNCF, get exercise, and raise money for a great cause. This will still allow you to keep the important UNCF work. "While the country is practicing social distancing, the event will engage participants in activities to walk, run, cycle and dance safely—all the while having fun and still raising money for UNCF."
Show your support in Alabama by donating, participate individually, form or join a team. To register for the virtual walk, visit UNCF.org/NationalWalk Be sure you sign up under the Birmingham region.
(Source) Press Release from UNCF sent to Townsquare Media.
LOOK: Here are the biggest HBCUs in America
More than 100 historically Black colleges and universities are designated by the U.S. Department of Education, meeting the definition of a school "established prior to 1964, whose principal mission was, and is, the education of black Americans."
StudySoup compiled the 20 largest historically Black colleges and universities in the nation, based on 2021 data from the U.S. Department of Education's National Center for Education Statistics. Each HBCU on this list is a four-year institution, and the schools are ranked by the total student enrollment.
LOOK: 28 Modern Black History Makers & Moments
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LOOK: 50 essential civil rights speeches
Many of the speakers had a lifetime commitment to human rights, but one tried to silence an activist lobbying for voting rights, before later signing off on major civil rights legislation. Several fought for freedom for more than one oppressed group.
Keep reading to discover 50 essential civil rights speeches.
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