The underlying story of "Richie Rich" is as old as the hills: The poor little rich boy has all the expensive toys he needs but is lonely and neglected, and has no playmates his own age. What's sort of wonderful is the way this movie takes that old formula and makes it fresh and new, with actors who give it wit and charm.
One of those actors is Macaulay Culkin, whose recent string of bombs ("The Nutcracker," "The Good Son," "The Pagemaster") almost made me forget that the kid does have an engaging screen personality when he isn't shoehorned into the wrong projects. This is his comeback, and possibly the last film in which he will be able to play a little boy. (Clues: He gets his first zit and notices that an aerobics instructor, played by Claudia Schiffer, has certain qualities far beyond those of a buddy.) Time marches on, and someday we'll be able to see little Macaulay with a beard and a cigarette.
In "Richie Rich," he plays the richest kid in the world. He lives in a mansion of incalculable luxury, with his parents and his personal valet, and one of the surprises of the movie is that these characters are not the stuffy, distant stereotypes that we would expect in this genre. Instead, they're warm and funny, and Richie likes them. His dad is played by Edward Herrmann (looking more than ever like a benevolent Franklin Roosevelt); his mom is Christine Ebersole, and Jonathan Hyde plays Cadbury, the valet, who looks after Master Richie with the discretion of Jeeves and the devotion of Mr. Watson.
Rich Industries is one of those movie conglomerates that manufactures lots and lots of everything. Down in the basement of the mansion, the brilliant Professor Keenbeam (Michael McShane) works on prototypes for still more brilliant inventions, including a robot bee, and Richie has fun visiting him.
But Richie is not happy. He's a naturally gifted baseball player but never gets to play with anybody his own age, and one day, while representing his father at a factory opening, he sees some kids playing a sandlot game across the street. He wants to join in, but they scorn him, and Cadbury ushers him back aboard the corporate helicopter.
The story of course has a villain. His name is Laurence Van Dough, and he's played by the invaluable John Larroquette as a slick, oily executive with Rich Industries, who schemes to sabotage the Rich family plane and take control of the company for himself. For a time, it appears his scheme has worked - Richie's parents disappear in the Bermuda triangle - but Richie wasn't on the plane and takes control of the company himself.
It goes without saying that the kid can make better decisions than the board of directors, and soon Rich Industries' profits are higher than ever. Richie invites his poor inner-city friends over to the house to play (they like the Kid-a-Pult, which hurtles them through the air).
Meanwhile, mom and dad float around on a rubber raft, making the best of the situation ("We're out of Perrier and caviar," Herrmann dolefully tells Ebersole, inspecting the emergency rations. "We're down to the Champagne.") "Richie Rich" contains no elements of startling originality, but director Donald Petrie, the script and the actors supply what such an enterprise needs above everything else: style. The movie is amusing precisely because it knows exactly what it is, and never tries too hard or strays into cheap payoffs.
Richie's world - his toys, his staff, his new playmates, even the danger he's in at the hands of the scheming villain - are all seen with a certain sunniness. The movie has the lightness of a 1930s fantasy and none of the hamhanded obviousness of many modern movies about kids. I was a little amazed at how much I enjoyed it.


Researchers have developed bees that naturally fight Varroa mites, foulbrood and chalkbrood. Here's how you can make it work in your hives.
When shopping for honeybees, beekeepers typically consider honey production, general temperament and winter tolerance. Certainly, those qualities are important, but others matter just as much. Thanks to the efforts of entomologists and queen breeders, backyard beekeepers now have access to bees with some unusual—and useful—genetic traits.
Consider, for instance, the Varroa mite—long one of beekeeping's biggest headaches. Beekeepers have relied on synthetic miticides and nonchemical methods to control these eight-legged vampires, but researchers working on the problem discovered that some honeybees were naturally better equipped to fight mites than others. They then painstakingly selected for the trait known as Varroa Sensitive Hygiene, also called VSH.
Just how does it work? Turns out VSH bees can detect when brood cells have been parasitized by Varroa mites.
Usually, the mites' activities—feeding on young bees during their development within capped, brood cells—go unnoticed. Then, parasitized adult bees emerge, weakened and potentially diseased, and adult mites (pictured above, greatly magnified) emerge along with them. Finally, female mites find new, open brood cells in which to lay their eggs, and the process begins again. Left unchecked, Varroa mites can devastate the colonies they inhabit.
But, sensing shenanigans, VSH bees uncap affected cells, remove parasitized brood and mites, and clean out the cells for a fresh start. While these special bees won't catch every last mite, they do significantly reduce a colony's overall mite loads.
Minnesota hygienic bees behave similarly; however, they are able to detect the presence of American foulbrood and chalkbrood in cells. Developed at the University of Minnesota's Bee Research Facility by Dr. Marla Spivak, Minnesota hygienic bees uncap affected cells and remove infected brood, which helps arrest the spread of disease.
Are you considering trying VSH or Minnesota hygienic bees in your apiary? Individual queens or queens and nucs with these specialized traits seem to be more common than full bee packages. They can be pricey, too. A single, marked VSH queen can cost as much as $50 with shipping. Shop around, and make sure the breeder you choose has been state-inspected and complies with your state's agricultural rules.
What exactly happens during those first six weeks? Well, it takes 21 days for her initial offspring to emerge. Then, another few weeks must pass to enable population numbers to build and for the new bees to age into their various roles within the colony.
Should something happen to your queen, the colony will attempt to replace her. Their replacement queen will then mate with area drones, and, although some of the original queen's specialized traits could remain, these probably will be lost with subsequent queens. In other words? For consistent hygienic behavior, you'll need to re-queen with marked, mated queens from a VSH or Minnesota hygienic queen breeder.
You do have at least one other option. If you have a local beekeeping club, compare notes about the kinds of bees currently being kept in your area, and experiment with different varieties. If you find one that works well, consider going in together to purchase several queens with the same characteristics.
"If a number of hobbyist beekeepers in one area get together and start working cooperatively so that they all have the same type of stock, the drones in that area would all have the same traits," Reuter explains.


text:
You are here: Home » Firearms » Long Range Target Rifles » Elcho Shield Rifles, 1878
Elcho Shield Rifles, 1878
Parent Category: Firearms
Category: Long Range Target Rifles
American riflemen using their breech loading rifles in long range competition beat Ireland at Creedmoor (USA) in 1874 and again at Dollymount (Ireland) in 1875. In the Centennial Match of 1876 at Creedmoor they were again successful beating teams from Australia, Canada, Ireland and Scotland. A Great Britain team was also beaten at Creedmoor in 1877. Despite this British riflemen were slow to adopt the breech loader for match rifle shooting.
Elite national shots in teams of eight from England, Ireland and Scotland competed annually for the Elcho Shield, firing at 800, 900 and 1000 yards. Study of the records of this competition allows some insight into the development of the match rifle. The following report on the trials for the English Eight held at Avonmouth, Bristol, makes some observation on rifles used:
"Not only were the competitors the elite of our national shots, but the weapons employed formed a more valuable collection than has yet been brought together, comprising the best rifles whose serviceable qualities had already been established, and some novelties in the gunsmith's art. Gibbs's famous local factory supplies Metford muzzle-loaders and Metford-Farquharson breech-loaders, while the Westley Richards Company have a new application of a Westley breech to Metford's barrel. This "Deely-Edge," as it is called after the names of the joint inventors, has a remarkably good breech action, and is very likely to become a prominent favourite when produced for Public sale. There are also a couple of Sharp's American breech loader, and an Ingram's (Scotch) muzzle-loader. Notwithstanding the greater amount of labour in the preparation of the charges and minute attention required, breech-loaders appear at least to maintain their popularity among connoisseurs." (Bristol Mercury, 6 June 1878)
In the Elcho Shield match that year five of the English Eight fired breech loaders; used were three Metford's, a Remington and a Sharps (the remaining three used Metford muzzle loaders). This was the first year that breech-loaders were used in the match.
Ireland won scoring 1610 against England (1560) and Scotland (1552). The Irish Team, with one exception who used a Metford muzzle loader, fired with Rigby muzzle loaders. The Scottish team used muzzle loaders by Rigby (3), Metford (2), Fraser-Ingram, Ingram and McCririck.
Some further contemporary observations regarding breech loaders follow:
"The shooting of the present year has been marked by a large use of breech-loading match rifles, principally of American manufacture. In the hands of Mr Humphry, for instance, the Remington rifle produced some wonderful scores. On the other hand, the brilliant shooting of the Irish team in the Elcho Challenge Shield matches, all of whom used the Rigby muzzle-loader, except Major Young, who used a Metford, shows that the muzzle-loader, if equalled in accuracy, is certainly unsurpassed at present. Indeed, after the conclusion of the match Mr John Rigby, the best shot of the team, and the inventor and maker of the rifle bearing his name, claims that the result of the match showed that there was nothing in the fuss that was being made over these new weapons. At the same time he made an announcement which will interest all small-bore men – namely, that he hoped to have ready before the next meeting of the Association a breechloading match rifle which should do as well as the muzzle loader. The only objection that has been urged so far against the American breech-loader is that it is subject to an occasional wild shot, producing a miss which the user knows is not due to any fault of his own. If Mr Rigby succeeds in obviating this defect, he will fulfil his promise of producing a breech-loading rifle equal to the muzzle-loader." (Taunton Courier, and Western Advertiser, 24 July 1878)
It was not until 1881 that breech-loaders were exclusively used in the match.

redpajama_set_name: RedPajamaCommonCrawl


text:
Above, a 1934 plaque from the Big Apple Night Club at West 135th Street and Seventh Avenue in Harlem. Discarded as trash in 2006.
• Grammar Nazi (7/16)
• TINA (There Is No Alternative) (7/16)
• "At the gym, I decided to hop on a treadmill. Got weird looks, so I started jogging instead" (7/16)
• "The person who would proof read Hitler's speeches was a grammar Nazi" (7/16)
• "I went to the grocery store. The sign said 'no food or drink inside.' So I went home" (7/16)
More new entries...
Follow @barrypopik
Above, Big Apple Corner at 54th Street and Broadway in Manhattan.
Above, John J. Fitz Gerald, from the Oct. 17, 1931, Turf Play, p. 7.
Listen to Robert Emmerich introduce "The Big Apple," a hit song from 1937. Music written by Bob and performed by Tommy Dorsey's Clambake Seven with Bob on piano. Lyrics written by Buddy Bernier and sung by Edythe Wright. Audio provided by Dorothy Emmerich.
Also listen to a 1937 "The Big Apple" song by Ozzie Nelson and his Orchestra. See another 1931 photo of John J. Fitz Gerald.
This site is edited by Barry Popik.
Entry from August 22, 2016
Patrolman Irving Francis ("Irv") Hayden provided rush hour traffic reports on Chicago's WGN radio in the 1960s. He coined the term "gapers' block" (cited in print since at least 1962) for traffic congestion caused by vehicles that slow down to "gape" at an accident. Gapers Block was also the title of a Chicago news blog that operated from 2003 until January 2016.
Hayden was killed when his helicopter crashed on August 10, 1971.
Wiktionary: gapers' block
gapers' block (plural gapers' blocks)
1. (chiefly US and Canada, idiomatic) A traffic jam resulting from motorists slowing to look at a motor vehicle collision or other roadside distraction.
This term is especially common in the Chicago vicinity.
Wikipedia: Gapers Block
Gapers Block (Gapers Block Media, LLC) is a Chicago-centric web publication focused on covering Chicago culture under the tag line: "Slow down and check out Chicago". The site, gapersblock.com, lists local events, aggregates other Chicago blogs and news of local interest and features many topical blogs: A/C (arts and culture), Drive-Thru (food related), Transmission (local music), Mechanics (state and local politics), Tailgate (sports coverage), and Book Club (book club and literary scene coverage).
Conceived in 2003 by Andrew Huff and Naz Hamid, the stuck-in-traffic themed section names, such as Merge (blog, links aggregation), Slowdown (calendar event listings), and Rearview (noteworthy local photos), are inspired by the Chicago-coined term, "gapers' block", a synonym (with "gapers' delay") for rubbernecking. (...) In December 2015, editor and publisher Andrew Huff announced the site would be going on "indefinite hiatus" on January 1, 2016.
2 August 1962, Chicago (IL) Daily Tribune, "Highbrows Are Getting the Lowdown: Taking Lessons from Flying Cop" by Hal Foust, pt. 1, pg. 12, cols. 2-4:
The policeman is Irv Hayden, a high school graduate who broadcasts rush hour guidance to motorists from the W-G-N helicopter.
Among the Haydenisms nominated for academic recognition are: "gapers block," "backup," "loosening," and "tight pocket."
10 February 1963, Chicago (IL) Sunday Tribune, "Copter Cop—an Eye in the Sky" by Larry Wolters, pt. 8-TV, col. 1:
FOUR HOURS every week-day Irv Hayden, W-G-N's 'copter cop, is "Someone to Watch Over Me," as the song says. He's up there, morning and evening during the busiest times, cruising around reporting by radio on traffic snarls and problems to make driving a little easier and safer.
"Gapers block" is probably the best known one. That's what Hayden calls a mile-long crawl of motorists who have slowed down to view the details of an accident.
9 October 1965, Chicago (IL) Tribune, "Opera Opening" by Eleanor Page, sec. 1, pg. 15, col. 1:
Some of the women must have felt like so many salmon fighting their way upstream as they wormed their way through the "gaper's block" as the rush hour traffic put it, in the lobby.
11 August 1971, Chicago (IL) Tribune, pg. 1, col. 1:
WGN Copter Crash;2 Die
Irv Hayden, Pilot Killed in Bellwood
By DAVID SCHNEIDEMAN
A fiery helicopter crash yesterday took the lives of WGN Radio's "Eye in the Sky," Patrolman Irwin (Irv) Hayden, and the helicopter's pilot in a vacant suburban Bellwood lot.
Hayden, 52, of 1536 W. Thoma Av., whose voice was familiar to millions of rush hour motorists during the last 11 years, and Demarest were trapped in the flaming wreckage.
The holdup in traffic caused by drivers slowing down to see what happened in an accident or other event on the road. See also rubberneck.
The wrecked car was cleared from the road but the traffic was still slowed due to the gaper's block.
by J W August 02, 2005
WGN Plus
Irv Hayden
POSTED 1:34 PM, DECEMBER 8, 2014, BY ANASSERIWGNAM, UPDATED AT 01:33PM, DECEMBER 8, 2014
Irv Hayden was trafficopter reporter attached to the Safety Education Division of the Chicago Police Department. Hayden and his pilot died in a crash in 1971 after their helilcopter struck a utility pole in the Chicago suburb of Bellwood.
Posted by Barry Popik
New York City • Streets • Monday, August 22, 2016 • Permalink


text:
module Fuzzer
  module Handshake
    class HandshakeError < StandardError
    end

    class Type
      attr_reader :id

      def self.parse(type, data)
        case type
          when 1;  ClientHello
          when 2;  ServerHello
          when 3;  HelloVerifyRequest
          when 11; Certificate
          when 12; ServerKeyExchange
          when 13; CertificateRequest
          when 14; ServerHelloDone
          when 15; CertificateVerify
          when 16; ClientKeyExchange
          when 20; Finished
          else
            fail HandshakeError, "parse: Unknown handshake packet: #{type}"
        end.parse(data)
      end

      public

      def initialize(id)
        @id = id
      end

      def to_wire
        fail HandshakeError, "to_wire: Not implemented: #{self.class}"
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
El Torneig de Tòquio 2022, oficialment conegut com a Rakuten Japan Open 2022, és un esdeveniment de tennis masculí disputat sobre pista dura que pertany a les sèries 500 de l'ATP. La 48a edició del torneig es va celebrar entre el 3 i el 9 d'octubre del 2022 a l'Ariake Coliseum de Tòquio (Japó).

El tennista estatunidenc Taylor Fritz va guanyar el tercer títol de la temporada.

Quadre individual masculí

Caps de sèrie

Quadre

Quadre dobles masculins

Caps de sèrie

Quadre

Enllaços externs
 Web oficial

2022
ATP Tour 2022

redpajama_set_name: RedPajamaWikipedia


text:
Cornell says $12.8 million in CARES Act funds will go to students
Matt Steecker
ITHACA - Cornell University is set to receive $12.8 million from the federal government as part of the coronavirus stimulus package, and plans to use the money to support students.
Cornell said Wednesday the university plans to use 100% of the funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act "Education Stabilization Fund" to support students, surpassing the federal requirements that half of the funds are set aside for emergency financial assistance for students.
The day before, President Donald Trump criticized another Ivy League institution — Harvard University — for accepting $8.6 million, because the university has an endowment of $40 billion, according to the Boston Herald. Trump said the taxpayer funds allocated to Harvard should instead be given to small businesses.
Following the criticism from Trump, Harvard officials announced Wednesday afternoon that the university will return the funds from the federal coronavirus aid package. Another prestigious university, Stanford University, withdrew its application for CARES Act funds.
Other colleges and universities around the country have been allocated stimulus funds as well. During the pandemic, institutions have taken blows to their finances as they have had to shut down campuses, provide housing refunds, and deal with lost revenue sources such as spring sports competitions.
"First, half of it has to go directly to students, so we need to see how institutions decide to distribute those monies, since the guidelines are quite flexible," said Victor Borden, a professor of educational leadership and policy studies at Indiana University. "Second, the other half will help these institutions minimize job cuts which, depending on the institution, can include everything from custodial to physical plant, student support services, residential and food services."
After having announced hiring and salary freezes in March, Cornell officials now say the university will likely furlough and lay off staff.
The New York Times reported U.S. Education Secretary Betsy DeVos shared concerns to Trump that the federal funding for universities with large endowments would be a bad use of taxpayer dollars.
"Cornell has a need-blind admissions policy, and we commit to meet the full financial needs of our students," said Joel Malina, Cornell vice president for university relations, in a statement. "We know that many of our students will have increased need as a result of the pandemic."
Cornell receives federal and state appropriations of over $150 million each year and has an endowment over $7 billion.
NY has ordered residents to stay home. Here's what you need to know Coronavirus live updates: Tompkins County has 11 confirmed cases of COVID-19 As coronavirus spreads, unafraid Ithaca Hikers still take to trails Ithaca school cancellations, closures and updates Ithaca cancellations, closures and updates
The amount of federal stimulus funding for each university was determined through a formula established by Congress based on the number of our low-income students receiving Pell Grants and the overall size of the student population.
In the 2016-17 academic year, 16% of all Cornell undergraduates, or 2,386 students, received Pell grants, according to CNBC. Cornell awarded close to $267 million in grants to its undergraduate students in the 2018-19 school year. The average debt for Cornell students who take loans is around $24,000.
"Even as our Ithaca campus faces an anticipated COVID-related budget shortfall of over $100 million for the coming fiscal year, we aim to guarantee that every single one, currently enrolled or newly admitted, has the financial resources to complete their Cornell education," Malina said.
Follow Matt Steecker on Twitter @OnTheStecord. Support our journalism and become a digital subscriber today. Click here for our special offers.


text:
Should Mountain West Teams Play BYU This Year?
By Jeremy Mauss July 12, 2020 8:06 am
By Jeremy Mauss | July 12, 2020 8:06 am MT
[jwplayer GVk0Ajhu-sNi3MVSU]
Help out a former conference league mate?
Cougars need some help
The college football season is gonna get weird and that could be fun, and this is still assuming we will have a college football season. The Big Ten and Pac-12 have canceled its non-league games for a lot of schools.
One school that has been hit the hardest, outside of Hawaii, is former Mountain West team BYU. The Cougars have lost five games this year and could lose more as they also have Missouri which seems destined to be a loss. That would cut its season in half to six games.
Hey #BYUFootball, how desperate are you for some games? pic.twitter.com/CRkGx5hNl5
— Mountain West Wire (@MWCwire) July 10, 2020
BYU is a team that being an independent to struggle to find games this year to fill out a full schedule and what that looks like is uncertain. Odds are that these conference-only arrangements expand to more leagues and possibly to all of college football.
A nine-game set up seems likely as that is a good number of games for each conference to play enough to determine division winners and then have a conference title game.
A way that BYU could fit into the Mountain West would be if their standards for testing and other protocols are similar to each other. Distance has nothing to do with playing games but being on the same page regarding player and staff health.
This is where the Mountain West could help out BYU and if we are being honest, getting the Cougars on the schedule is a bump in TV ratings and a solid opponent.
There are plenty of fans of Mountain West schools who would like to tell the Cougars off but this could be an opportunity for both sides.
BYU already has three Mountain West teams on the schedule with Boise State, San Diego State, and Utah State. Getting six more teams should not be that hard.
There is a straightforward way and a creative way to make this work. The straightforward way is to have BYU fill in the gaps with six other Mountain West teams.
That schedule could look like and the dates are clearly tentative and we will match up with other Mountain West teams.
Plus, we are on the assumption that all leagues will be playing each other exclusively and the season starts the second week in September and adding a week in December.
9/12 – Wyoming
09/19 – at Colorado State
10/02 – Utah State
10/10 – at Nevada
10/17 – Fresno State
11/06 – at Boise State
11/14 – San Diego State
11/28 – at UNLV
12/5 – San Jose State
All of the weeks above would fit into the schedule except the final two weeks would take some tweaking but not all that much.
AD's are not as optimistic from April for a #collegefootball seasonhttps://t.co/zDedhn9SbA
Temporary Conference Member?
The more extreme idea comes with BYU coming in as a one-year member of the league and join the Mountain Division. This would create a 13-team league but it can totally work with a nine-game schedule.
This would not change schedules all that much since the conference normally plays eight games with six divisions and two cross-division games. This would not impact the current schedules. Having BYU join the Mountain Division, those teams would play its same schedule plus add the Cougars to get to nine. The West Division would either add BYU and one more Mountain Division to get to nine overall.
This would mean that the Cougars could play for the Mountain West title. That scenario would be beyond weird but why the heck not for just one year since COVID-19 is making anything but normal.
People won't like it but why not do something out of the box and if this could actually work, maybe it will mend the fences even more between BYU and the Mountain West to have a better relationship.
BYU Football, Mountain West Football, NCAAF


text:
\section{Introduction}

Person re-identification (re-id) is about matching identity classes in
detected person bounding box images from non-overlapping camera views
over distributed open spaces.
This is an inherently challenging task because person visual
appearance may change dramatically in different camera views from
different locations due to unknown changes in human pose,
illumination, occlusion, and background clutter \cite{gong2014person}.
Existing person re-id studies typically focus on either feature representation
\cite{ELF_ECCV08,SDALF_CVPR10,colorInvariants,SalienceReId_CVPR13,liao2015person,GOG,ma2017person} or
matching distance metrics
\cite{KISSME_CVPR12,xiong2014person,PRDC,wang2014person,Anton_2015_CoRR,zhang2016learning,wang2016highly,wang2016human,wang2016pami,chen2017person}
or their combination in deep learning framework
\cite{li2014deepreid,ahmed2015improved,wangjoint,xiao2016learning,subramaniam2016deep,chen2016multi}.
Regardless, the overall objective is to
obtain a view- and location-invariant (cross-domain) representation. We consider
that learning any matching distance metric is intrinsically learning a
global feature transformation across domains (two disjoint camera
views) therefore obtaining a ``normalised'' feature representation for
matching.

Most re-id features
are typically hand-crafted to
encode {\em local} topological and/or spatial structural information,
by different image decomposition schemes such as
horizontal stripes \cite{ELF_ECCV08,colorInvariants},
body parts \cite{SDALF_CVPR10},
and patches \cite{SalienceReId_CVPR13,GOG,liao2015person}.
These localised features are effective
for mitigating the person pose and detection misalignment in re-id matching.
More recent deep re-id models \cite{xiao2016learning,wangjoint,chen2016multi,ahmed2015improved}
benefit from the availability of larger scale datasets such as
CUHK03 \cite{li2014deepreid} and Market-1501 \cite{zheng2015scalable}
and from lessons learned on other vision
tasks \cite{krizhevsky2012imagenet,girshick2014rich}.
In contrast to {\em local} hand-crafted features,
deep models, in particular Convolutional Neural Networks
(CNN) \cite{lecun1998gradient}, favour intrinsically in learning {\em
global} feature representations with a few exceptions. They have been
shown to be effective for re-id.

We consider that either local or global feature learning {\em alone} is
suboptimal. This is motivated by the human visual system
that leverages both global (contextual) and local (saliency) information concurrently
\cite{navon1977forest,torralba2006contextual}.
This intuition for {\em joint learning} aims to extract correlated
complementary information in different context whilst {\em satisfying the
same learning constraint}\footnote{In person re-id context, the learning constraint
refers to the image person identity label supervision.}
therefore achieving more reliable recognition.
To that end, we need to address a number of non-trivial problems:
(i) the model learning behaviour in satisfying the same label
constraint may be different at the local and global levels;
(ii) any complementary correlation between local and global features
is unknown and may vary among individual instances, therefore must
be learned and optimised consistently across data;
(iii) People's appearance in public scenes is diverse in both pattens
and configurations. This makes it challenging to learn correlations between local and
global features {\em for all appearances}.

This work aims to formulate a deep learning model for jointly
optimising local and global feature selections concurrently and
to improve person re-id using {\em only} generic matching metrics such
as the L2 distance. We explore a deep learning approach for its
potential superiority in learning from large scale data
\cite{xiao2016learning,chen2016multi}.
For the bounding box image based person re-id,
we consider the entire person in the bounding box as a {\em global scene context} and body
parts of the person as {\em local information sources}, both are subject to
the surrounding background clutter within a bounding box, and potentially
also misalignment and partial occlusion from bounding box detection.
In this setting, we wish to discover and optimise
jointly correlated complementary feature selections in the local and global
representations, both subject to the same label constraint concurrently. Whilst the
former aims to address pose/detection misalignment and occlusion by localised
fine-grained saliency information, the latter exploits holistic
coarse-grained context for more robust global matching.

To that end, we formulate a deep two-branch CNN architecture,
with one branch for learning localised feature selection (local branch)
and the other for learning global feature selection (global branch).
Importantly, the two branches are not independent but
synergistically correlated and jointly learned concurrently.
This is achieved by: (i) imposing inter-branch interaction between the
local and global branches, and (ii) enforcing a separate learning
objective loss function to each branch
for learning independent discriminative capabilities, whilst being
subject to the same class label constraint.
Under such balancing between interaction and independence,
we allow both branches to be learned concurrently
for maximising their joint optimal extraction and selection of different
discriminative features for person re-id.
We call this model the {\bf Joint Learning Multi-Loss} (JLML) CNN model.
To minimise poor learning due to inherent noise
and potential covariance, we introduce a structured feature selective
and discriminative learning mechanism into both the local and global
branches subject to a joint sparsity regularisation.

The {\bf contributions} of this work are:
{\bf (I)} We propose the idea of learning concurrently both local and global
feature selections for optimising feature discriminative capabilities
in different context whilst performing the same person re-id tasks. This is
currently under-studied in the person re-id literature to our best knowledge.
{\bf (II)}
We formulate a novel {\em Joint Learning Multi-Loss} (JLML) CNN model
for not only learning both global and local discriminative features in
different context by optimising multiple classification losses
on the same person label information concurrently, but also utilising their
complementary advantages jointly in coping with local misalignment and optimising
holistic matching criteria for person re-id.
{\bf (III)}
We introduce a structured sparsity based feature selection learning mechanism
for improving multi-loss joint feature learning robustness w.r.t. noise and data
covariance between local and global representations.
Extensive comparative evaluations demonstrate the superiority of the
proposed JLML model over a wide range of existing state-of-the-art
re-id models on five benchmark datasets VIPeR \cite{ELF_ECCV08}, GRID \cite{loy2009multi},
CUHK01~\cite{li2012human}, CUHK03~\cite{li2014deepreid}, and Market-1501~\cite{zheng2015scalable}.

\section{Related Works}
The proposed JLML model considers
learning both local and global feature selections jointly for optimising
their correlated complementary advantages. This goes beyond existing methods
mostly relying on only one level of feature representation.
Specifically, the JLML method is related to the saliency learning
based models \cite{SalienceReId_CVPR13,WangEtAl_BMVC14} in terms of
modelling localised part importance.
However, these existing methods consider only the patch appearance statistics within individual
locations but no global feature representation learning,
let alone the correlation and complementary information discovery
between local and global features as modelled by the JLML.

Whilst the more recent Spatially Constrained Similarity (SCS) model
\cite{chen2016similarity}
and Multi-Channel Parts (MCP) network \cite{Cheng_TCP} consider both levels
of representation, the JLML model differs significantly from them:
{\bf (i)} The SCS method focuses on supervised metric learning,
whilst the JLML aims at joint discriminative feature learning
and needs only generic metrics for re-id matching.
Also, hand-crafted local and global features are extracted {\em separately} in SCS
without any inter-feature interaction and correlation learning involved,
as opposite to the joint learning of global and local feature selections concurrently
subject to the same supervision information in the JLML;
{\bf (ii)} The local and global branches of the MCP model are supervised and
optimised by a triplet ranking loss, in contrast to
the proposed multiple classification loss design (Sec. \ref{sec:JLML_model}).
Critically, this one-loss model learning is likely to impose negative influence on the discriminative
feature learning behaviour for both branches
due to potential over-low pre-branch independence and over-high inter-branch correlation.
This may lead to suboptimal joint learning of local and global feature selections in model optimisation,
as suggested by our evaluation in Section \ref{sec:ablation}.
{\bf (iii)} In addition, the JLML is capable of performing structured feature sparsity
regularisation along with the multi-loss joint learning of local and global feature
selections for providing additional benefits (Sec. \ref{sec:ablation}).
Whilst similar in theory to the sparsity constraint on the supervised SCS metric learning,
we perform differently sparse generic feature learning
{\em without} the need for supervised metric optimisation.

In terms of loss function, the HER model \cite{wang2016highly}
similarly does not exploit pair-wise re-id labels but defines
a single identity label per training person for
{\em regression loss} (vs. the classification loss in the JLML)
based re-id feature embedding optimisation.
Importantly, HER relies on the pre-defined feature (mostly hand-crafted local feature)
{\em without} the capability of jointly learning global and local feature
representations and discovering their correlated complementary advantages
as specifically designed in JLML.
Also, the DGD \cite{xiao2016learning} model uses
the classification loss for model optimisation.
However, this model considers only the global feature representation learning
of {\em one-loss} classification
as opposite to the proposed joint global and local feature learning of {\em multi-loss} classification
concurrently subject to maximising the same person identity matching.

\section{Model Design}

\subsection{Problem Definition}

We assume a set of $n$ training images
$\mathcal{I} = \{ \bm{I}_i \}_{i=1}^n$ with the corresponding
identity labels as $\mathcal{Y} = \{y_i\}_{i=1}^n$.
These training images capture the visual appearance of
$n_\text{id}$ (where $y_i \in [1,\cdots,
n_\text{id}]$) different people
under non-overlapping camera views.
We formulate a Joint Learning Multi-Loss (JLML) CNN model that
aims to discover and capture concurrently
complementary discriminative information about a person image from
both local and global visual features of the image in order to
optimise person re-id under significant viewing
condition changes across locations.
This is in contrast to most existing re-id methods typically depending
only on either local or global features alone.

\subsection{Joint Learning Multi-Loss}
\label{sec:JLML_model}

\begin{figure} [h]
	\centering
	\includegraphics[width=1.00\linewidth]{images/fig_pipeline.pdf}
	\vskip -0.1cm
	\caption{\footnotesize
		The Joint Learning Multi-Loss (JLML) CNN model architecture.

	}
	\label{fig:pipline}
\end{figure}

The overall design of the proposed JLML model is depicted in
Figure \ref{fig:pipline}. This JLML model consists of a two-branches CNN network:
(1) One {\em local branch} of $m$ streams of an identical structure
with each stream learning the most discriminative local visual
features for one of $m$ local image regions of a person bounding box image;
(2) Another {\em global branch} responsible for learning the most
discriminative global level features from the entire person image.
For concurrently optimising
per-branch discriminative feature representations and
discovering correlated complementary information between local and
global feature selections, a {\em joint learning} scheme that subjects
both local and global branches to the same identity label supervision
is considered with two underlying principles:

\vspace{0.1cm}
\noindent \textit{(I) Shared low-level features.}
We construct the global and local branches on a shared lower conv
layer, in particular the first conv layer\footnote{We found
empirically no clear benefits from increasing the number of shared
conv layers in our implementation.}, for facilitating inter-branch
common learning.
The intuition is that, the lower conv layers capture low-level
features such as edges and corners which are common to all patterns in
the same images. This shared learning is similar in spirit to
multi-task learning \cite{argyriou2007multi}, where
the local and global feature learning branches are two related learning tasks.
Sharing the low-level conv layer reduces the model parameter size
therefore model overfitting risks. This is especially critical in
learning person re-id models when labelled training data is limited.

\vspace{0.1cm}
\noindent \textit{(II) Multi-task independent learning subject to
shared label constraints.}
To maximise the learning of complementary discriminative features from
local and global representations, the remaining layers of the two
branches are learned independently subject to given identity labels. That
is, the JLML model aims to learn concurrently multiple identity
feature representations for different local image regions and the
entire image, all of which aim to maximise the {\em same} identity
matching {\em both} individually
and collectively at the same time.
Independent multi-task learning aims to preserve both local saliency
in feature selection and global robustness in image
representation.
To that end, the JLML model is designed to perform {\em multi-task
independent learning subject to shared identity label constraints} by
allocating each branch with a separate objective loss function.
By doing so, the per-branch learning behaviour is conditioned
independently on
the respective feature representation. We call this branch-wise loss
formulation as the {\bf MultiLoss} design.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1.3}
	\setlength{\tabcolsep}{0.07 cm}
	\vspace{-.5cm}
	\caption{\footnotesize
		JLML-ResNet39.
		MP: Max-Pooling; AP: Average-Pooling;
		S: Stride; SL: Slice; CA: Concatenation; G: Global; L: Local.
	}
	\vskip 0pt
	\begin{tabular}{|c|c||c|c|c|}
		\hline
		Layer \# & Layer & Output Size & Global Branch & Local Branch \\ \hline \hline
		1 & conv1      & $112\!\!\times\!\!112$  &\multicolumn{2}{c|}{$3\!\!\times\!\!3$, 32, S-2} \\ \hline
		\multirow{4}{*}{9} & \multirow{4}{*}{conv2\_x}  & \multirow{3}{*}{G: $56\!\!\times\!\!56$}  &$3\!\!\times\!\!3$ MP, S-2 & SL-4, $2\!\!\times\!\!2$ MP, S-1 \\ \cline{4-5}
		& & \multirow{3}{*}{L: $28\!\!\times\!\!56$}   &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 32 \\  3\!\!\times\!\!3, 32 \\ 1\!\!\times\!\!1, 64 \\ \end{bmatrix} \!\!\times\!\! 3$} &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 16 \\  3\!\!\times\!\!3, 16 \\ 1\!\!\times\!\!1, 32 \\ \end{bmatrix} \!\!\times\!\! 3$}    \\
		& &   &    &      \\
		& &   &    &      \\ \hline
		\multirow{3}{*}{9}& \multirow{3}{*}{conv3\_x}  & \multirow{2}{*}{G: $28\!\!\times\!\!28$}    &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 64 \\  3\!\!\times\!\!3, 64 \\ 1\!\!\times\!\!1, 128 \\ \end{bmatrix} \!\!\times\!\! 3$} &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 32 \\  3\!\!\times\!\!3, 32 \\ 1\!\!\times\!\!1, 64 \\ \end{bmatrix} \!\!\times\!\! 3$}   \\
		& & \multirow{2}{*}{L: $14\!\!\times\!\!28$} & &\\
		& &   &    &      \\ \hline
		\multirow{3}{*}{9}& \multirow{3}{*}{conv4\_x}  & \multirow{2}{*}{G: $14\!\!\times\!\!14$}    &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 128 \\  3\!\!\times\!\!3, 128 \\ 1\!\!\times\!\!1, 256 \\ \end{bmatrix} \!\!\times\!\! 3$} &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 64 \\  3\!\!\times\!\!3, 64 \\ 1\!\!\times\!\!1, 128 \\ \end{bmatrix} \!\!\times\!\! 3$}   \\
		& & \multirow{2}{*}{L: $7\!\!\times\!\!14$} & & \\
		& &   &    &      \\ \hline
		\multirow{3}{*}{9}& \multirow{3}{*}{conv5\_x}  & \multirow{2}{*}{G: $7\!\!\times\!\!7$}    &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 256 \\  3\!\!\times\!\!3, 256 \\ 1\!\!\times\!\!1, 512 \\ \end{bmatrix} \!\!\times\!\! 3$} &  \multirow{3}{*}{$\begin{bmatrix} 1\!\!\times\!\!1, 128 \\  3\!\!\times\!\!3, 128 \\ 1\!\!\times\!\!1, 256 \\ \end{bmatrix} \!\!\times\!\! 3$}   \\
		& & \multirow{2}{*}{L: $4\!\!\times\!\!7$} & & \\
		& &   &    &      \\ \hline
		\multirow{2}{*}{1}& \multirow{2}{*}{fc} & \multirow{2}{*}{$1\!\!\times\!\!1$} & $7\!\!\times\!\!7$ AP  &  $4\!\!\times\!\!7$ AP, CA-4 \\ \cline{4-5}
		& &  & $\begin{bmatrix} 1\!\!\times\!\!1, 512 \end{bmatrix}$ & $\begin{bmatrix} 1\!\!\times\!\!1, 512 \end{bmatrix}$   \\ \hline

		1 & fc & $1\!\!\times\!\!1$    & ID\# & ID\# \\ \hline
	\end{tabular
	\label{tab:model_arch}
	\vspace{-.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf Network Construction.}
We adopt the Residual CNN unit \cite{he2016deep} as the JLML's building blocks
due to its capacity for deeper model design whilst retaining a smaller
model parameter size\footnote{The choice of base network
is independent of our JLML model design. Other types, e.g.
GoogLeNet \cite{szegedy2015going} or VGG-Net \cite{simonyan2014very},
can be readily applied in our model.
}.
Specifically, we customise the ResNet50 architecture in both layer and
filter numbers and design the JLML model as a 39 layers ResNet ({\bf
JLML-ResNet39}) tailored for re-id tasks.
The configuration of JLML-ResNet39 is given in Table \ref{tab:model_arch}.
Note that, the ReLU rectification non-linearity \cite{krizhevsky2012imagenet}
after each conv layer is omitted for brevity.

\vspace{0.1cm}
\noindent {\bf Feature Selection.}
To optimise JLML model learning robustness against noise and diverse
data source, we introduce a feature selection capability in JLML by
a structure sparsity induced
regularization \cite{kong2014exclusive,wang2013multi}.
Our idea is to have a competing-to-survive mechanism in feature
learning that discourages irrelevant features whilst encourages
discriminative features concurrently in different local and
global context to maximise a shared identity matching objective.
To that end, we sparsify the global feature representation
with a group LASSO \cite{wang2013multi}:
\begin{equation}
	\ell_{2,1} = \| \bm{W}_G \|_{2,1} =
	\sum_{i=1}^{d_g} \| \bm{w}_g^i  \|_2
\end{equation}
where $\bm{W}_G = [\bm{w}_g^1, \cdots, \bm{w}_g^{d_g}] \in \mathcal{R}^{c_g \times d_g}$ is the parameter matrix of the global branch
feature layer taking
as input $d_g$ dimensional vectors from the previous layer
and outputting $c_g$ dimensional (512-D) feature representation.
Specifically, with the $\ell_1$ norm applied on the $\ell_2$ norm of $\bm{w}_g^i$,
our aim is to learn (tune) selectively feature dimension
importance subject to both the sparsity principle and the identity label
constraint simultaneously.

Similarly, we also enforce a local feature sparsity constraint by
an exclusive group LASSO \cite{kong2014exclusive}:
\begin{equation}
	\ell_{1,2}
	= \| \bm{W}_L \|_{1,2} =
	\sum_{i=1}^{c_l} \sum_{j=1}^{m} \| \bm{w}_{l,j}^i \|_1^2
\end{equation}
where

\begin{equation}
	\bm{W}_L =

	\begin{bmatrix}
		\bm{w}_{l,1}^{1\top} & \cdots & \bm{w}_{l,m}^{1\top} \\
		\cdots & \cdots & \cdots \\
		\bm{w}_{l,1}^{c_l\top}& \cdots & \bm{w}_{l,m}^{c_l\top}
	\end{bmatrix}
	= \begin{bmatrix}
		\bm{w}_{l}^{1\top} \\
		\cdots \\
		\bm{w}_{l}^{c_l\top}
	\end{bmatrix}

\end{equation}
is the parameter matrix of the local branch feature layer
with $m \times d_l$ and $c_l$ (512) as the input and output dimensions
($m$ the image stripe number).
The $\bm{w}_{l,j}^i \in \mathcal{R}^{d_l \times 1}$ defines
the parameter vector for contributing the $i$-th output feature dimension
from the $j$-th local input feature vector, $j \in [1, 2, \cdots, m]$.
In particular, the $\ell_{2,1}$ regulariser performs sparse feature selection
for individual image regions as below:
(1) We perform feature selective learning at the local region level
by enforcing the $\ell_1$ norm directly on $\bm{w}_{l,j}^i$,
conceptually similar to the group LASSO at the global level.
(2) We then apply a non-sparse smooth fusion with the $\ell_2$ norm
to combine the effects of different local features
weighted by the sparse $\bm{w}_{l,j}^i$.
(3) Lastly, we exploit the $\ell_1$ norm again
at the level of $\bm{w}_l^{k}$ ($k \in [1,2,\cdots,c_l]$)
to learn the local 512-D feature representation selection.
Figure \ref{fig:lasso} shows our structured sparsity
regularisations for both local and global feature selections.

\begin{figure} [h!]
	\centering
	\includegraphics[width=1.01\linewidth]{images/fig_lasso.pdf}
	\vskip -0.3cm
	\caption{\footnotesize
		Group sparsity regularisations on fc layer parameter matrices
		($\bm{W}_G$ for the global branch and
		$\bm{W}_L$ for the local branch)
		for selectively learning feature representations.

		Solid and dashed rectangles denote $\ell_2$ norm and
		$\ell_1$ norm respectively.
	}
	\label{fig:lasso}
\end{figure}

\vspace{0.1cm}
\noindent {\bf Loss Function. }
For model training,
we utilise the cross-entropy {\em classification} loss function
for both global and local branches so to optimise person {\em identity
classification} given training labels of multiple person classes
extracted from pair-wise labelled re-id dataset.
Formally,
we predict the posterior probability $\tilde{y}_i$ of image $\bm{I}_i$
over the given identity label $y_i$:
\begin{equation}
{p}(\tilde{y}_{i} = y_i | \bm{I}_{i}) = \frac{\exp(\bm{w}_{y_i}^{\top} \bm{x}_{i})} {\sum_{k=1}^{|n_\text{id}|} \exp(\bm{w}_{k}^{\top} \bm{x}_{i})}
\label{eq:prob}
\end{equation}
where $\bm{x}_{i}$ refers to the feature vector of $\bm{I}_i$ from the corresponding branch,
and $\bm{W}_k$ the prediction function parameter
of training identity class $k$.
The training loss on a batch of $n_\text{bs}$ images is computed as:
\begin{equation}
l = - \frac{1}{n_\text{bs}}\sum_{i=1}^{n_\text{bs}}  \log \Big(p(\tilde{y}_{i} = y_i|\bm{I}_{i}) \Big)
\label{eq:loss_cls}
\end{equation}
Combined with the group sparsity based feature selection regularisations,
we have the final loss function for the global and local branch
sub-networks as:
\begin{equation}
l_\text{global} = l + \lambda_\text{global} \| \bm{W}_G \|_{2,1}, \;\;
l_\text{local} = l + \lambda_\text{local} \| \bm{W}_L \|_{1,2}
\label{eq:loss}
\end{equation}
where $\lambda_\text{global}$ and $\lambda_\text{local}$
control the balance between the identity label loss and the feature
selection sparsity regularisation.
We empirically set $\lambda_\text{local} = \lambda_\text{global} = 5\!\!\times\!\!10^{-4}$
by cross-validation in our evaluations.

\vspace{0.1cm}
\noindent {\bf Choice of Loss Function.}
Our JLML model learning deploys a {\em classification} loss
function. This differs significantly from the {\em
contrastive} loss functions used by most existing deep re-id methods
designed to exploit pairwise re-id labels defined by {\em both} positive and
negative pairs, such as the pairwise verification
\cite{varior2016gated,subramaniam2016deep,ahmed2015improved,li2014deepreid},
triplet ranking \cite{Cheng_TCP},
or both \cite{wangjoint,chen2016multi}. Our JLML model training
does {\em not} use any labelled negative pairs inherent to all person
re-id training data, and we extract identity class labels from only
positive pairs.
The motivations for our JLML classification loss based learning are:
{\bf (i)} Significantly {\em simplified} training data batch
construction, e.g. random sampling with no notorious tricks required,
as shown by other deep classification methods \cite{krizhevsky2012imagenet}.
This makes our JLML model more scalable in real-world applications with
very large training population sizes when available.
This also eliminates the {\em undesirable} need for
carefully forming pairs and/or triplets in preparing re-id training
splits, as in most existing methods, due to the inherent imbalanced
negative and positive pair size distributions.
{\bf (ii)} Visual psychophysical findings suggest that
representations optimised for classification tasks generalise well to
novel categories \cite{edelman1998representation}. We consider that
re-id tasks are about model generalisation to unseen test identity classes
given training data on {\em independent} seen identity classes.
Our JLML model learning exploits this general classification learning
principle beyond the strict pair-wise relative verification loss in
existing re-id models.

\subsection{Model Training}
We adopt the standard Stochastic Gradient Descent (SGD) optimisation algorithm \cite{krizhevsky2012imagenet}
to perform the batch-wise joint learning of local and
global branches.
Note that, with SGD we can naturally synchronise the optimisation processes
of the two branches by constraining
their learning behaviours subject to the same identity label information
at each update.
This is likely to avoid representation learning divergence between two branches
and help enhance the correlated complementary learning capability.

\subsection{Re-Id by Generic Distance Metrics}
{Once the JLML model is learned,
we obtain a 1,024-D joint representation
by concatenating the local (512-D) and global (512-D) feature vectors
(the fc layer in Table~\ref{tab:model_arch}).}
For person
re-id, we deploy this 1,024-D deep feature representation using {\em only}
a generic distance metric {\em without} camera-pair specific distance
metric learning, e.g. L2 distance.
Specifically, given a test probe image $\bm{I}^p$ from one camera view
and a set of test gallery images
$\{\bm{I}_i^g\}$ from other non-overlapping camera views:
(1) We first compute their corresponding 1,024-D feature vectors by
forward-feeding the images to the trained JLML model,
denoted as $\bm{x}^p=[\bm{x}_g^p; \bm{x}_l^p]$ and
$\{\bm{x}_i^g=[\bm{x}_g^g; \bm{x}_l^g]\}$.
(2) We then compute L2 normalisation on the global and local features,
separately. (3) Lastly we compute the cross-camera matching
distances between $\bm{x}^p$ and $\bm{x}_i^g$ by some generic matching
metric, e.g. L2 distance. We then rank all
gallery images in ascendant order by their L2 distances to the probe image.
The probabilities of true matches of probe person images in Rank-1 and
among the higher ranks indicate the goodness of the learned
JLML deep features for person re-id tasks.

\section{Experiments}

\noindent {\bf Datasets.}
For evaluation, we used five benchmarking re-id datasets,
VIPeR~\cite{ELF_ECCV08},
GRID~\cite{loy2009multi},
CUHK01~\cite{li2012human},
CUHK03~\cite{li2014deepreid}, and
Market-1501~\cite{zheng2015scalable}.
Figure \ref{fig:dataset} shows some examples of person bounding box
images from these datasets.
The datasets are collected by different data sampling protocols
from different environments, where:
{\bf (a)} VIPeR has one image per person per view,
with low-resolution under severe lighting change.
{\bf (b)} GRID provides one image per person per view, with additional
images for 775 distracting persons under very poor lighting from underground stations.
{\bf (c)} CUHK01 contains two images person per view from a university campus.
{\bf (d)} CUHK03 consists of up to five images per person per view,
obtained by both manually labelled and auto-detected person bounding
boxes with the latter posing a more challenging re-id task due to
detection bounding box misalignment and occlusion.
{\bf (e)} Market-1501 has variable numbers of images per person per
view captured from a supermarket, with all bounding boxes
automatically detected.
These datasets present a wide range of re-id evaluation scenarios with
different population sizes under different challenging viewing
conditions (Table \ref{tab:dataset_stats}).

\begin{figure} [ht]
	\centering

	\subfigure[\scriptsize VIPeR]{
	\includegraphics[width=0.17\linewidth]{images/dataset_viper.jpg}
	}
	\subfigure[\scriptsize GRID]{
		\includegraphics[width=0.17\linewidth]{images/dataset_grid.jpg}
	}
	\subfigure[\scriptsize CUHK01]{
		\includegraphics[width=0.17\linewidth]{images/dataset_cuhk01.jpg}
	}
	\subfigure[\scriptsize CUHK03]{
		\includegraphics[width=0.17\linewidth]{images/dataset_cuhk03.jpg}
	}
	\subfigure[\scriptsize Market]{
		\includegraphics[width=0.17\linewidth]{images/dataset_market.jpg}
	}
	\vskip -0.5cm
	\caption{\footnotesize
		Example cross-view image pairs from five re-id datasets.

	}
	\label{fig:dataset}
\end{figure}
\begin{table}[h!] \footnotesize
	\centering

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.02cm}
	\vspace{-0.5cm}
	\caption{\footnotesize
		Settings of person re-id datasets.
		TS: Test Setting;
		SS: Single-Shot; MS: Multi-Shot.
		SQ: Single-Query; MQ: Multi-Query.
	}
	\vskip 0pt
	\begin{tabular}{|c||c|c|c|c|c|c|c|}
		\hline
		Dataset  &
		{Cams} &
		{IDs} &
		{Train IDs} &
		{Test IDs} &
		{Labelled} &
		{Detected} &
		TS \\ \hline \hline %
		VIPeR & 2 & 632 & 316 & 316 & 1,264 & 0 & SS \\
		GRID & 8 & 250 & 125 & 125 & 1,275 & 0 & SS \\
		CUHK01 & 2 & 971 & 871/485 & 100/486 & 1,942 & 0 & SS/MS\\
		CUHK03 & 6 & 1,467 & 1,367 & 100 & 14,097 & 14,097 & SS\\
		Market & 6  & 1,501& 751 & 750 & 0 & 32,668 & SQ/MQ \\
		\hline
	\end{tabular
	\label{tab:dataset_stats}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf Evaluation Protocol.}
We adopted the standard supervised re-id setting to evaluate the proposed JLML model
(Sec. \ref{sec:eval_specific}).
The training and test data splits and testing settings of each dataset is given in Table \ref{tab:dataset_stats}.
Specifically, on VIPeR, we split randomly the whole population (632 people) into two halves: One for training (316) and another for testing (316).
We repeated $10$ trials of random people splits and used the averaged results.
On CUHK01, we considered two training/test splits:
485/486 \cite{liao2015person}
and 871/100 \cite{ahmed2015improved}.
Again, we reported the results averaged over 10 random trials for either split.
On GRID, the training/test split were 125/125 with 775 distractor people included in the test gallery.
We used the benchmarking 10 people splits \cite{loy2009multi} and the averaged performance.
On CUHK03, following \cite{li2014deepreid} we repeated 20 times of
random 1260/100 training/test splits and reported the averaged
accuracies under the single-shot evaluation setting.
On Market-1501, we used the standard training/test split (750/751) \cite{zheng2015scalable}.
We used the cumulative matching characteristic (CMC) to measure
re-id accuracy on all benchmarks, except on Market-1501 we also used
in addition the recall measure of multiple truth matches by mean
Average Precision (mAP), i.e. first computing the area under the
Precision-Recall curve for each probe, then calculating the mean of
Average Precision over all probes \cite{zheng2015scalable}.

\vspace{0.1cm}
\noindent {\bf Competitors.}
We compared the JLML model against $10$ existing
state-of-the-art methods as listed in Table \ref{tab:methods}.
They range from hand-crafted and deep learning features to
domain-specific distance metric learning methods.
We summarise them into three categories:
{\bf (A)} Hand-crafted (feature) with domain-specific distance learning (metric);
{\bf (B)} Deep learning (feature) with domain-specific deep verification metric learning;
{\bf (C)} Deep learning (feature) with generic non-learning L2 distance (metric).

\begin{table} [h!]
	\centering
	\scriptsize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.025 cm}
	\vspace{-.5cm}
	\caption{\footnotesize
		Person re-id method categorisation by features and metrics.

		Cat: Category;
		DL: Deep Learning;
		CPSL: Camera-Pair Specific Learning;
		DVM: Deep Verification Metric;
		DVM,L2: Ensemble of DVM and L2;
		CHS: Fusion of Colour, HOG, SILPT features.

	}
	\vskip 0pt
	\begin{tabular}{|c||c|c|c|c|c|}
		\hline
		\multirow{2}{*}{Cat}
		& \multirow{2}{*}{Method}
		& \multicolumn{2}{c|}{Feature} & \multicolumn{2}{c|}{Metric} \\
		\cline{3-6}
		& & Hand-Crafted & DL & CPSL & Generic \\
		\hline
		\hline
		%
		\multirow{4}{*}{A}
		& {\bf XQDA} \cite{liao2015person}
		& LOMO & -
		& XQDA & - \\
		%
		& {\bf GOG} \cite{matsukawa2016hierarchical}
		& GOG & -
		& XQDA & - \\
		%
		%
		& {\bf NFST} \cite{zhang2016learning}
		& LOMO, KCCA & -
		& NSFT & - \\
		%
		& {\bf SCS} \cite{chen2016similarity}
		& CHS & -
		& SCS & - \\
		\hline
		%

		\multirow{3}{*}{B}
		& {\bf DCNN+} \cite{ahmed2015improved}
		& - & DCNN+
		& DVM & - \\
		%
		& {\bf X-Corr} \cite{subramaniam2016deep}
		& - & X-Corr
		& DVM & - \\
		& {\bf MTDnet} \cite{chen2016multi}
		& - & MTDnet
		& DVM, L2 & - \\
		%
		\hline
		%
		\multirow{4}{*}{C}
		& {\bf S-CNN} \cite{varior2016gated}
		& - & S-CNN
		& - & L2 \\
		%
		& {\bf DGD} \cite{xiao2016learning}
		& - & DGD
		& - & L2 \\

		& {\bf MCP} \cite{Cheng_TCP}
		& - & MCP
		& - & L2 \\
		%

		& {\bf JLML} (Ours)
		& - & {JLML}
		& - & L2 \\
		\hline
	\end{tabular
	\label{tab:methods}
	\vspace{-.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf Implementation.}
We used the Caffe framework~\cite{jia2014caffe} for our JLML model implementation.
We started by pre-training the JLML model on ImageNet (ILSVRC2012).
Subsequently, for CUHK03 or Market, we used only their own training data
for model fine-tuning, i.e. ImageNet $\rightarrow$ CUHK03/Market;
For CUHK01 or VIPeR or GRID, we pre-trained JLML on CUHK03+Market (whole datasets),
and then fine-tuned on their respective training images, i.e.
ImageNet $\rightarrow$ CUHK03+Market $\rightarrow$ CUHK01 / VIPeR / GRID.
All input person images were resized to $224\!\times\!224$ in pixel.
For local branch, according to a coarse body part layout
we evenly decomposed the whole shared convolutional feature maps (i.e. the entire image)
into four ($m=4$) horizontal strip-regions.
We used the same parameter settings (summarised in Table \ref{tab:param})
for pre-training and training the JLML model on all datasets.
We also adopted the stepped learning rate policy,
e.g. dropping the learning rate by a factor of 10 every 100K iterations for JLML pre-training
and every 20K iterations for JLML training.
We utilised the L2 distance as the default matching metric, unless stated otherwise.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.2 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		JLML training parameters. BLR: base learning rate; LRP: learning rate policy;
		MOT: momentum; IT: iteration; BS: batch size.
		%

	}
	\vskip 0pt
	\begin{tabular}{|c||c|c|c|c|c|}
		\hline
		Parameter
		& BLR & LRP & MOT & IT \# & BS \\ \hline \hline
		Pre-train
		& 0.01 & step (0.1, 100K) & 0.9 & 300K & 32 \\ \hline
		Train
		& 0.01 & step (0.1, 20K) & 0.9 & 50K & 32 \\
		\hline
	\end{tabular
	\label{tab:param}
	\vspace{-0.3cm}
\end{table}

\subsection{Conventional Intra-Domain Re-Id Evaluations}
\label{sec:eval_specific}
We conducted extensively comparative evaluations on conventional
supervised learning based person re-id tasks.

\vspace{0.1cm}
\noindent {\bf (I) Evaluation on CUHK03. }
Table~\ref{tab:res_cuhk03} shows the comparisons of JLML against $8$
existing methods on CUHK03.
It is evident that JLML outperforms existing methods in all categories
on both labelled and detected bounding boxes,
surpassing the 2nd best performers DGD and X-Corr on corresponding labelled and
detected images in Rank-1 by $7.9\%$(83.2-75.3) and $8.6\%$(80.6-72.0)
respectively.
X-Corr/GOG/JLML also suffer the least from auto-detection
misalignment, indicating the robustness and competitiveness of the
joint learning approach to mining complementary local and global
discriminative features.

\begin{table}
	\centering

	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.1 cm}
	\vspace{-.3cm}
	\caption{\footnotesize
		CUHK03 evaluation. $1^\text{st}/2^\text{nd}$ best in
		red/blue.

	}
	\vskip 0pt
	\begin{tabular}{|c||c|cccc|cccc|}
		\hline

		\multirow{2}{*}{Cat}
		& Annotation &  \multicolumn{4}{c|}{Labelled} &\multicolumn{4}{c|}{Detected} \\ \cline{2-10}
		& Rank (\%) & R1 & R5 & R10 & R20 & R1 & R5 & R10 & R20 \\ \hline \hline
		\multirow{3}{*}{A}
		& XQDA & 55.2  & 77.1  &  86.8 &  83.1   & 46.3  & 78.9  &  83.5 &  93.2 \\
		%
		& GOG & 67.3  & 91.0  &  96.0 &  -   & 65.5  & 88.4  &  93.7 &  - \\
		%

		& NSFT & 62.5  & 90.0  & 94.8 &  98.1   & 54.7  & 84.7 &  \color{blue}  94.8 &  95.2 \\
		\hline
		\multirow{3}{*}{B}
		& DCNN+ & 54.7  & 86.5  &  93.9 &  98.1   & 44.9  & 76.0  &  83.5 &  93.2 \\
		& X-Corr & 72.4  & 95.5  &  - &  \color{blue}  98.4  &  \color{blue}  72.0  &  \color{blue}  96.0  &  - &  \color{blue}   98.2 \\

		& MTDnet & 74.7 &  \color{blue}  96.0 &  \color{blue}   97.5 &  - &  -   & -  & -  &  - \\
		\hline

		\multirow{3}{*}{C}
		& S-CNN & -  & -  &  - &  -   & 68.1  & 88.1  &  94.6 &  -\\
		& DGD &  \color{blue}   75.3  & -  &  - &  - & -  & -  &  - & - \\

		& {\bf JLML} & \color{red} 83.2 & \color{red} 98.0 & \color{red}  99.4 &  \color{red}  99.8
		&  \color{red}  80.6 &  \color{red}  96.9 &  \color{red}  98.7 &   \color{red}  99.2 \\
		\hline
	\end{tabular
	\label{tab:res_cuhk03}
	\vspace{-.1cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (II) Evaluation on Market-1501. }
We evaluated JLML against four existing models on Market-1501.
Table \ref{tab:res_market} shows the clear performance superiority of JLML
over all state-of-the-arts with more significant Rank-1 advantages
over other methods compared to CUHK03, giving $19.3\%$(85.1-65.8) (SQ) and
$13.7\%$(89.7-76.0) (MQ) gains over the 2nd best S-CNN.
This further validates the advantages of our joint learning of
multi-loss classification for optimising re-id especially when the
re-id test population size increases ($751$ people on Market-1501
vs. $100$ people on CUHK03).

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.3 cm}
	\vspace{-.5cm}
	\caption{\footnotesize

		Market-1501 evaluation. $1^\text{st}/2^\text{nd}$ best in red/blue.
		All person bounding box images were auto-detected.
	}
	\vskip 0pt
	\begin{tabular}{|c||c|cc|cc|}
		\hline

		\multirow{2}{*}{Cat}
		& Query Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \cline{2-6}
		& Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		\multirow{3}{*}{A}
		& XQDA &  43.8 &  22.2  &  54.1 &  28.4\\
		%
		& SCS &  51.9 &  26.3 &  - &  -  \\
		%

		& NFST & 61.0 & 35.6  &  71.5 &  46.0  \\
		\hline

		\multirow{2}{*}{C}
		& S-CNN &  \color{blue}  65.8 &  \color{blue}  39.5  &  \color{blue}  76.0 &  \color{blue}  48.4  \\

		& {\bf JLML} &  \color{red} {85.1} &  \color{red} {65.5}  &   \color{red} {89.7} &   \color{red} {74.5}  \\
		\hline
	\end{tabular
	\label{tab:res_market}
	\vspace{-.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (III) Evaluation on CUHK01.}
We compared our JLML model with $8$ state-of-the-art methods on CUHK01.
Table \ref{tab:res_cuhk01} shows that
JLML surpasses clearly all compared models under both training/test splits in single- and multi-short settings.
Moreover, JLML outperforms in Rank-1 ($76.7\%$) the best hand-crafted
feature method NFST (R1 $69.1\%$) when the training population size is
small (486 people).
When the training population size increases (871 people),
JLML is even more effective than all deep competitors in exploiting
extra training classes by inducing more identity-discriminative joint person features in distinct context.
For example, JLML gains $5.8\%$(87.0-81.2) more Rank-1 than the 2nd
best method X-Corr in single-shot re-id, further improved the gain of
$4.8\%$(69.8-65.0) under the 486/485 split.
These results show consistent superiority and robustness of the proposed JLML model
over the existing methods.

\begin{table} [!h]
	\centering
	\footnotesize
	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.1 cm}
	\vspace{-.3cm}
	\caption{\footnotesize
		CUHK01 evaluation. $1^\text{st}/2^\text{nd}$ best in bold/typewriter.

	}
	\vskip 0pt
	\begin{tabular}{|c||c|cccc|cccc|}
		\hline

		\multirow{2}{*}{Cat}
		& Split &  \multicolumn{4}{c|}{871/100 split} &\multicolumn{4}{c|}{486/485 split} \\ \cline{2-10}
		& Rank (\%) & R1 & R5 & R10 & R20 & R1 & R5 & R10 & R20 \\ \hline \hline
		&  \multicolumn{9}{c|}{Single-Shot Testing Setting} \\ \cline{1-10}
		\multirow{1}{*}{A}
		%
		& GOG & -  & -  &  - &  -
		& 57.8 & 79.1 & 86.2 & 92.1 \\
		\hline
		\multirow{3}{*}{B}
		& DCNN+ & 65.0  & -  &  - &  -
		& 47.5  & 71.6  &  80.3 &  87.5 \\
		& X-Corr & \color{blue} 81.2  & \color{red} {97.3}  &  - & \color{blue} 98.6
		 & 65.0  & \color{red} {89.7}  &  - & 94.4 \\
		& MTDnet & 78.5 & 96.5 & \color{blue} 97.5 & - & -  & -  &  - &  - \\ \hline
		\multirow{3}{*}{C}

		& DGD & -  & -  &  - &  -
		& \color{blue} 66.6  & -  &  - &  -\\
		& MCP & -  & -  &  - &  -
		& 53.7  & 84.3  & \color{blue} 91.0 & \color{red} 96.3 \\

		& {\bf JLML} & \color{red} {87.0} & \color{blue} 97.2  & \color{red} {98.6} & \color{red} {99.4}
		& \color{red} {69.8 } & \color{blue} 88.4  & \color{red} {93.3} & \color{red} 96.3 \\

		\hline \hline
		&  \multicolumn{9}{c|}{Multi-Shot Testing Setting} \\ \cline{1-10}
		\multirow{3}{*}{A}
		& XQDA & -  & -  &  - &  -   & 63.2  & 83.9  &  90.0 &  94.2 \\
		& GOG & -  & -  &  - & - & 67.3  & \color{blue} 86.9  & \color{blue} 91.8 & \color{blue} 95.9 \\

		& NFST		& -  & -  & - & -   & \color{blue} 69.1  & \color{blue} 86.9 & \color{blue} 91.8 &  95.4 \\
		\hline
		\multirow{1}{*}{C}
		& {\bf JLML} & \color{red} 91.2 & \color{red} 98.4 & \color{red} 99.2 & \color{red} 99.8
		& \color{red} 76.7 & \color{red} 92.6 & \color{red} 95.6 & \color{red} 98.1 \\
		\hline
	\end{tabular
	\label{tab:res_cuhk01}
	\vspace{-.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (IV) Evaluation on VIPeR. }
We evaluated the performance of JLML against $8$ strong competitors on VIPeR,
a more challenging test scenario with fewer training
classes ($316$ people) and lower image resolution.
On this dataset, the best performers are
hand-crafted feature methods (SCS and NFST)
rather than deep models. This is in contrast to the tests on CUHK01,
CUHK03, and Market-1501.
This is due to (i) the small training data insufficient for learning
effectively discriminative deep models with millions of parameters;
(ii) the greater disparity to CUHK03 in camera viewing conditions
which makes knowledge transfer less effective (see Implementation).
Nevertheless, the JLML model remains the best among all deep
methods with or without deep verification metric learning.
This validates the superiority and robustness of our deep joint global
and local representation learning of multi-loss classification
given sparse training data.
We attribute this property to the JLML's capability of
mining complementary features in different context for both
handling local misalignment and optimising global matching.

\begin{table} [!h]
	\centering
	\footnotesize
	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.35 cm}
	\vspace{-.3cm}
	\caption{\footnotesize
		VIPeR evaluation. $1^\text{st}/2^\text{nd}$ best in red/blue.
	}
	\vskip 0pt
	\begin{tabular}{|c||c|cccc|}
		\hline

		\multirow{1}{*}{Cat}
		& Rank (\%) & R1 & R5 & R10 & R20 \\ \hline \hline
		\multirow{4}{*}{A}
		& XQDA 	& 40.0  & 68.1  &  80.5 &  91.1 \\
		%
		& GOG 	& 49.7  & -  &  88.7 &  94.5 \\
		%

		& NFST 	&  \color{blue}  51.1  &  \color{blue}  82.1  &  \color{blue}  90.5 &  \color{blue}  95.9 \\
		& SCS 	& \color{red} 53.5  & \color{red} 82.6  & \color{red} 91.5 & \color{red} 96.7 \\
		\hline
		\multirow{2}{*}{B}
		& DCNN+ & 34.8  & 63.6  &  75.6 &  84.5 \\

		& MTDnet & 47.5 & 73.1 & 82.6 & - \\
		\hline
		\multirow{3}{*}{C}
		& MCP & 47.8  & 74.7  & 84.8 &  91.1 \\

		& DGD & 38.6  & -  &  - &  -\\
		& {\bf JLML}  & 50.2  & 74.2 & 84.3 & 91.6 \\

		\hline
	\end{tabular
	\label{tab:res_viper}
	\vspace{-.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (V) Evaluation on GRID. }
We compared JLML against $4$ competing methods on GRID\footnote{The
GRID dataset has not been evaluated as extensively as others like
VIPeR / CUHK01 / CUHK03, although GRID provides a more realistic test
setting with
a large number of distractors in testing.
One possible reason is the more challenging re-id setting imposed by GRID resulting in significantly poorer matching rates by all published methods
(see \url{http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html}),
also as verified by our evaluation in Table \ref{tab:res_grid}.
}.
In addition to poor image resolution, poor lighting and
a small training size ($125$ people),
GRID also has extra distractors in the testing population therefore
presenting a very challenging but realistic re-id scenario.
Table \ref{tab:res_grid} shows a significant superiority of JLML
over existing state-of-the-arts, with Rank-1 $12.8\%$(37.5-24.7)
better than the 2nd best method GOG, a $51.8\%$ relative improvement.
This demonstrates the unique and practically desirable advantage of JLML in handling
more realistically challenging open-world re-id matching where
large numbers of distractors are usually present.
It is worth pointing out that this step-change advantage in re-id matching rate on GRID
is achieved by deep learning from only a limited number of training
identity classes with highly imbalanced images sampled from $8$ distributed camera views,
e.g. $25$ images from the $6^\text{th}$ camera vs.
$513$ from the $5^\text{th}$ camera.
This imbalanced sampling directly results in
not only scarce pairwise training data but also
insufficient training samples for pairwise camera views, resulting in
significant degradation in re-id performance from {\em all} pairwise
supervised learning based models XQDA, GOG, SCS, and X-Corr.
In contrast, JLML is designed to avoid the need for pairwise labelled
information in model learning by instead learning from multi-loss classifications.
Moreover, the joint learning of multi-loss classification benefits
from concurrent local and global feature selections in different
context, resulting in more robust and accurate re-id matching in a
heterogeneous search space.

\begin{table} [!h]
	\centering
	\footnotesize
	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.35 cm}
	\vspace{-.5cm}
	\caption{\footnotesize
		GRID evaluation.
		$1^\text{st}/2^\text{nd}$ best in red/blue.
	}
	\vskip 0pt
	\begin{tabular}{|c||c|cccc|}
		\hline

		\multirow{1}{*}{Cat}
		& Rank (\%) & R1 & R5 & R10 & R20 \\ \hline \hline
		\multirow{3}{*}{A}
		& XQDA 	& 16.6 & 33.8 & 41.8 & 52.4  \\

		& GOG 	&  \color{blue}  24.7 &  \color{blue}  47.0 &  \color{blue}  58.4 &  \color{blue}  69.0 \\
		%

		& SCS 	& 24.2 & 44.6 & 54.1 & 65.2 \\

		\hline
		\multirow{1}{*}{B}
		& {X-Corr}  & 19.2  & 38.4 & 53.6 & 66.4 \\
		\hline
		\multirow{1}{*}{C}
		& {\bf JLML}  &   \color{red} 37.5 &  \color{red} 61.4  &  \color{red} 69.4  &   \color{red} 77.4  \\

		\hline
	\end{tabular
	\label{tab:res_grid}
	\vspace{-.3cm}
\end{table}

\subsection{CNN Architecture Comparisons}
We compared the proposed JLML-ResNet39 model with
four seminal classification CNN architectures
(Alexnet \cite{krizhevsky2012imagenet},
VGG16 \cite{simonyan2014very},
GoogLeNet \cite{szegedy2015going}, and
ResNet50 \cite{he2016deep}) in model size and complexity.
Table \ref{tab:base_nets} shows that
the JLML has both the $2^\text{nd}$ smallest model size (7.2 million parameters)
and the $2^\text{nd}$ smallest FLOPs ($1.54\!\times\! 10^9$),
although containing more streams (5 vs. 1 in all other CNNs)
and more layers ($39$, more than all except ResNet50).

\begin{table} [!h]
	\centering

	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.12 cm}
	\vspace{-0.3cm}
	\caption{ \footnotesize
		Comparisons of model size and complexity.
		FLOPs: the number of FLoating-point OPerations;
		PN: Parameter Number.
	}
	\vskip 0pt
	\begin{tabular}{|c||c|c|c|c|}
		\hline

		%
		%
		Model & FLOPs & PN (million) & Depth & Stream \#
		\\ \hline
		AlexNet &  \bf 7.25$\times$10$^8$ & 58.3 & 7 & 1 \\
		VGG16 & 1.55$\times$10$^{10}$ & 134.2 & 16 & 1 \\
		ResNet50 & 3.80$\times$10$^9$ & 23.5 & \bf 50 & 1 \\
		GoogLeNet & 1.57$\times$10$^9$ & \bf 6.0 & 22 & 1 \\ \hline
		{\bf JLML-ResNet39} & 1.54$\times$10$^9$ & 7.2 & 39 & \bf 5 \\
		\hline
	\end{tabular
	\label{tab:base_nets}
	\vspace{-0.3cm}
\end{table}

\subsection{Further Analysis and Discussions}
\label{sec:ablation}
We further examined the component effects of our JLML model
on Market-1501 in the following aspects.

\vspace{0.1cm}
\noindent {\bf (I) Complementary Benefits of Global and Local Features.}
We evaluated the complementary effects of our jointly learned local and global features
by comparing their individual re-id performance against that of the joint features.
Table \ref{tab:complementary} shows:
{\bf (i)} Any of the two feature representations {\em alone} is
competitive for re-id, e.g. the local JLML feature surpasses S-CNN (Table \ref{tab:res_market})
by Rank-1 $13.1\%$(78.9-65.8) (SQ) and $10.4\%$(86.4-76.0) (MQ);
and by mAP $18.3\%$(57.8-39.5) (SQ) and $20.0\%$(68.4-48.4) (MQ).
{\bf (ii)} A further performance gain is obtained from the joint
feature representation, yielding further $6.2\%$(85.1-78.9) (SQ) and
$3.3\%$(89.7-86.4) (MQ) in Rank-1 increase, and $7.7\%$(65.5-57.8)
(SQ) and $6.1\%$(74.5-68.4) (MQ) in mAP boost.
These results show the complementary advantages of jointly learning
the local and global features in different context using the JLML
model.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.4 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Complementary benefits of global and local features.
	}
	\vskip 0pt
	\begin{tabular}{|c||cc|cc|}
		\hline

		Query Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\  \cline{1-5
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline

		JLML (Global) & 77.4 & 56.0 & 85.0 & 66.0\\
		JLML (Local) &  78.9& 57.8 & 86.4 & 68.4\\ \hline
		JLML (joint) & \textbf{85.1} &  \textbf{65.5}  &  \textbf{89.7} &  \textbf{74.5} \\
		\hline
	\end{tabular
	\label{tab:complementary}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (II) Importance of Branch Independence.}
We evaluated the importance of branch independence by comparing our {\em MultiLoss} design
with a {\em UniLoss} design that merges
two branches into a single loss \cite{Cheng_TCP}.
Table \ref{tab:individuality} shows that the proposed MultiLoss model significantly
improves the discriminative power of global and local re-id features, e.g. with
Rank-1 increase of $9.0\%$(85.1-76.1) (SQ) and $6.0\%$(89.7-83.7) (MQ);
and mAP improvement of $13.3\%$(65.5-52.2) (SQ) and $11.7\%$(74.5-62.8) (MQ).
This shows that branch independence plays a critical role in
joint learning of multi-loss classification for effective feature optimisation.
One plausible reason is due to the negative effect of a single loss imposed
on the learning behaviour of both branches, caused by the potential divergence
in discriminative features in different context (local and global).
This is shown by the significant performance degradation of both global and local features
when the UniLoss model is imposed.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.2 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Importance of branch independence.
	}
	\vskip 0pt
	\begin{tabular}{|c|c||cc|cc|}
		\hline

		\multirow{2}{*}{Loss}
		& Query Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\  \cline{2-6
		& Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		\multirow{3}{*}{UniLoss}
		& Global Feature & 58.3 & 31.7 & 70.4 & 43.2\\
		& Local Feature &  46.3 & 26.3 & 58.0 & 34.0\\ \cline{2-6}
		& \bf Full & 76.1 & 52.2 & 83.7 & 62.8  \\
		\hline
		\multirow{3}{*}{\bf MultiLoss}
		& Global Feature & 77.4 & 56.0 & 85.0 & 66.0\\
		& Local Feature &  78.9& 57.8 & 86.4 & 68.4\\ \cline{2-6}
		& {\bf Full} & \textbf{85.1} &  \textbf{65.5}  &  \textbf{89.7} &  \textbf{74.5} \\
		\hline
	\end{tabular
	\label{tab:individuality}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (III) Benefits from Shared Low-Level Features.}
We evaluated the effects of interaction between global and local branches
introduced by the shared conv layer (common ground) by deliberately
removing it and then comparing the re-id performance.
Table \ref{tab:common_conv} shows the benefits from
jointly learning low-level features in the common conv layers,
e.g. improving Rank-1 by $1.9\%$(85.1-83.2) / $1.4\%$(89.7-88.3)
and mAP by $2.4\%$(65.5-63.1) / $2.4\%$(74.5-72.1)
for single-/multi-query re-id.
This confirms a similar finding as in multi-task learning study \cite{argyriou2007multi}.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.3 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Benefits from shared low-level features.
	}
	\vskip 0pt
	\begin{tabular}{|c|cc|cc|}
		\hline

		Query Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \hline
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		{\bf Without} Shared Feature & 83.2 & 63.1 & 88.3 & 72.1
		\\ \hline
		{\bf With} Shared Feature &
		\textbf{85.1} &  \textbf{65.5}  &  \textbf{89.7} &  \textbf{74.5} \\ \hline
	\end{tabular
	\label{tab:common_conv}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (IV) Effects of Selective Feature Learning. }
We evaluated the contribution of our structured sparsity
based Selective Feature Learning (SFL)
(Eq. \eqref{eq:loss}).
Table \ref{tab:selective_learn} shows that our SFL mechanism can
bring additional re-id matching benefits, e.g. improving Rank-1 rate
by $1.7\%$(85.1-83.4) (SQ) and $1.0\%$(89.7-88.7) (MQ); and mAP by
$1.7\%$(65.5-63.8) (SQ) and $1.6\%$(74.5-72.9) (MQ).

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.4 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Effects of selective feature learning (SFL).
	}
	\vskip 0pt
	\begin{tabular}{|c|cc|cc|}
		\hline

		Query Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \hline
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		Without SFL &  83.4 &  63.8  &  88.7 &  72.9\\ \hline
		{\bf With SFL} &  \textbf{85.1} &  \textbf{65.5}  &  \textbf{89.7} &  \textbf{74.5}\\
		\hline
	\end{tabular
	\label{tab:selective_learn}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (V) Choice of Generic Matching Metrics.}
We evaluated the choice of generic matching distances on person re-id
using the full JLML feature.
Table \ref{tab:match_dist} shows that L1 and L2 generate very similar and competitive re-id matching accuracies.
This suggests the flexibility of the JLML model in adopting generic matching metrics.

\begin{table} [!h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.4 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Effects of generic matching metrics.
	}
	\vskip 0pt
	\begin{tabular}{|c|cc|cc|}
		\hline

		Query-Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \hline
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline

		L1 & 84.9 & 65.3 & 89.2 & 74.6 \\ \hline
		L2 &  {85.1} &  {65.5}  &  {89.7} & {74.5}\\
		\hline
	\end{tabular
	\label{tab:match_dist}
	\vspace{-0.1cm}
\end{table}

\noindent {\bf (VI) Effects of Body Parts Number.}
{We evaluated the sensitivity of local decomposition,
	i.e. body parts number $m$.
	Table \ref{tab:num_body_part} shows that the decomposition of 4 body-parts is the optimal choice, approximately corresponding to head+shoulder, upper-body, upper-leg and lower-leg (Figure \ref{fig:local_parts}).}

\begin{table} [h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.4 cm}
	\vspace{-0.3cm}
	\caption{\footnotesize
		Effects of body parts number.
	}
	\vskip 0pt
	\begin{tabular}{|c|cc|cc|}
		\hline

		Query-Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \hline
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		2 & 83.9 & 64.4 & 88.8 & 72.9 \\ \hline
		4 & \bf 85.1 &  \bf 65.5  &  \bf 89.7 & \bf 74.5 \\\hline
		6 & 83.4 &  62.6  &  88.5 & 71.8 \\\hline
		8 &  82.3 &  61.3  &  87.4 & 70.7 \\\hline
		10 &  81.7 &  60.4  &  87.2 & 69.8 \\
		\hline
	\end{tabular
	\label{tab:num_body_part}
	\vspace{-0.3cm}
\end{table}

\begin{figure}[h]
	\centering
    \includegraphics[width=1.0\linewidth]{images/market_stripes_10.jpg}
	\vskip -0.3cm
	\caption{\footnotesize
		Visualisation of the optimal body part decomposition.
	}
	\label{fig:local_parts}
\end{figure}

\vspace{0.1cm}
\noindent {\bf (VII) Complementary Effects between JLML Deep Features
	and Supervised Metric Learning. }
{We evaluated the complementary effects of the JLML deep features
	and conventional supervised metric learning (XQDA \cite{liao2015person},
	KISSME \cite{KISSME_CVPR12}, and CRAFT \cite{chen2017person}).
	Results from Table \ref{tab:metric_learning} show that:
	(1) Given strong deep learning features such as JLML,
	additional distance metric learning does not benefit further from the same training data.
	(2) Moreover, it may even suffer from some adversary effect.}

\begin{table} [h]
	\centering
	\footnotesize

	\renewcommand{\arraystretch}{1}
	\setlength{\tabcolsep}{0.4 cm}

	\caption{\footnotesize
	Complementary of JLML features and metric learning.
	}
	\vskip 0pt
	\begin{tabular}{|c|cc|cc|}
		\hline

		Query-Type &  \multicolumn{2}{c|}{Single-Query} &\multicolumn{2}{c|}{Multi-Query} \\ \hline
		Measure (\%)
		& R1 & mAP & R1 & mAP  \\ \hline \hline
		KISSME
		& 82.1 & 61.4 & 87.5 & 70.2\\
		XQDA
		& 82.6 & 63.2 & 88.2 & 72.4\\
		CRAFT
		& 77.9 & 56.4 & - & -\\
		\hline
		L2 & \bf 85.1 &  \bf 65.5  &  \bf 89.7 & \bf 74.5 \\
		\hline
	\end{tabular
	\label{tab:metric_learning}
	\vspace{-0.3cm}
\end{table}

\vspace{0.1cm}
\noindent {\bf (VIII) Local Features vs. Global Features.}
{A strength of the local features is the capability
	of mitigating misalignment and occlusion, as compared to the global features.
	This is inherently learned from data by the JLML local branch.
	Figure \ref{fig:local_global} shows the single-query re-id results on six randomly selected probe persons with misalignment and/or occlusion.

	It is evident that the local features achieve better re-id matching ranks than the global counterparts in most cases.
	This clearly demonstrates the robustness of local features against the misalignment of and occlusion within a person bounding box.}

\begin{figure} [h]
	\centering
	\includegraphics[width=1.0\linewidth]{images/local-vs-global.pdf}
	\vskip -0.3cm
	\caption{\footnotesize

		Comparing the gallery true match ranks of each probe image (single-query) with occlusion and/or misalignment by the local and global features. Each probe may have multiple truth matches in the gallery. Smaller numbers mean better ranking performances.
	}
	\label{fig:local_global}
\end{figure}

\vspace{0.1cm}
\noindent {\bf (IX) Feature Extraction Time Cost. }
{The average time for extracting JLML feature
	is 2.75 milliseconds per image (364 images per second)
	on a Nvidia Pascal P100 GPU card.}

\section{Conclusion}
In this work, we presented a novel Joint Learning of Multi-Loss (JLML)
CNN model (JLML-ResNet39)
for person re-identification feature learning.
In contrast to existing re-id approaches
that often employ either global or local appearance features alone,
the proposed model is capable of extracting and exploiting both and maximising their correlated complementary effects by
learning discriminative feature representations in different context subject to
multi-loss classification objectives in a unified framework.
This is made possible by the proposed JLML-ResNet39 architecture design.
Moreover, we introduce a structured sparsity based feature selective learning mechanism
to reduce feature redundancy and further improve the joint feature selections.
Extensive comparative
evaluations on five re-id benchmark datasets
were conducted to validate the advantages of
the proposed JLML model over a wide range of the state-of-the-art methods
on both manually labelled and more challenging auto-detected person images.
We also provided component evaluations and analysis of model
performance in order to give insights on the model design.

\small
\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


Now that the real estate market has crashed, Nicolas Cage is selling off all the ridiculous homes he bought at the top of the bubble. We think it's pretty funny, too!
Did Nic Cage's Bel Air mansion fall out of escrow? In October, listing agent Stephen Shapiro confirmed there was an accepted offer on the Copa De Oro home following a sealed bid auction. An email blast from the MLS notes that "It's Back on the Market," and indeed, the listing has returned.
It's back to its original pricechopped asking price of $17.5 million. And just in time for the holidays. What a a drag for Cage, who is said to be trying to unload multiple properties.


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