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2012 2011 1 2 3 4 5 The Season
Women in Film Max Mara Face of the Future Award®
WOMEN IN FILM AND MAX MARA ANNOUNCE HAILEE STEINFELD
AS THE RECIPIENT OF 2013 WOMEN IN FILM
MAX MARA FACE OF THE FUTURE AWARD®
Actress Hailee Steinfeld will be the next recipient of the Women In Film Max Mara Face of the Future Award® in recognition of her outstanding acting achievement and embodiment of style and grace. She will be honored at the 2013 Women In Film Crystal + Lucy Awards® on Wednesday, June 12th at The Beverly Hilton Hotel in Beverly Hills, California. Steinfeld will travel to Milan, Italy to meet the Maramotti family and will attend the Fall Winter 2013 Max Mara Fashion Show on Thursday, February 21st.
In 2010, Hailee Steinfeld emerged as a breakout star with her poignant Academy Award® nominated performance as Mattie Ross in the Coen Brothers' remake of "True Grit." Her turn opposite Jeff Bridges, Matt Damon, and Josh Brolin garnered her universal acclaim with dozens of film critic award wins, including the Broadcast Film Critics Association Award, the Chicago Film Critics Association Award, the Toronto Film Critics Association Award, and the Vancouver Film Critics Circle Award for Best Supporting Actress. In addition to her Oscar® nomination, Steinfeld received a Screen Actors Guild® nomination for Outstanding Performance by a Female Actor in a Supporting Role, as well as a BAFTA Award® nomination for Best Actress in a Leading Role.
Steinfeld is currently in production on "3 Days to Kill" opposite Kevin Costner. She is in the upcoming drama "Hateship, Friendship" opposite Guy Pearce and Kristen Wiig. She recently wrapped production on John Carney's "Can a Song Save Your Life?" alongside Keira Knightley and Mark Ruffalo. Later this year, she will star as Juliet in Carlo Carlei's adaptation of "Romeo & Juliet". Also in November 2013, Steinfeld will be seen in Gavin Hood's highly anticipated sci-fi action adventure "Ender's Game."
Max Mara, the premiere collection of the Max Mara Fashion Group, is the epitome of Italian style and luxury; a confident and contemporary collection of tailored silhouettes, sumptuous fabrications and couture details applied to ready-to-wear. Known for stylish coats, form-skimming separates, sharp suits and elegant accessories, Max Mara, was founded by the late visionary Achille Maramotti in 1951. The collection is now available in 2,334 locations in more than 100 countries and was the first of 19 collections under the Max Mara umbrella. The company remains privately held and managed by the Maramotti family.
Women In Film, Los Angeles (WIF) is a non-profit organization dedicated to helping women achieve their highest potential within the global entertainment, communication and media industries and to preserving the legacy of women within those industries. Founded in 1973, WIF and its Women In Film Foundation provide for members an extensive network of contacts, educational programs, scholarships, film finishing funds and grants, access to employment opportunities, mentorships and numerous practical services in support of this mission. In the independent film world, the organization focuses on assisting female independent filmmakers who have exhibited advanced and innovative skills, specifically through its Film Finishing Fund and award-winning PSA program.
The Women In Film Max Mara Face of the Future Award®, inaugurated at Women In Film's 2006 Crystal + Lucy Awards®, is given to an actress who is experiencing a turning point in her career through her work in the film and television industries, through her contributions to the community at large, in recognition of her outstanding achievements, and her embodiment of style and grace. Past recipients have been Chloë Grace Moretz, Katie Holmes, Zoë Saldana, Elizabeth Banks, Ginnifer Goodwin, Emily Blunt and Maria Bello.
For additional information on Max Mara, please contact Michelle McWilliams or Molly Biscone at mcwilliams.m@usa.maxmara.com/ biscone.m@usa.maxmara.com
For additional information on Women In Film, please contact Catherine Olim or Elise Freimuth at PMK-BNC Catherine.Olim@pmkbnc.com / Elise.Freimuth@pmkbnc.com


White-Variegated Hosta features dainty spikes of lavender bell-shaped flowers rising above the foliage in mid summer. Its attractive twisted narrow leaves remain forest green in colour with prominent white stripes and tinges of light green throughout the season. The fruit is not ornamentally significant.
White-Variegated Hosta is a dense herbaceous perennial with tall flower stalks held atop a low mound of foliage. Its medium texture blends into the garden, but can always be balanced by a couple of finer or coarser plants for an effective composition.
White-Variegated Hosta will grow to be about 18 inches tall at maturity extending to 24 inches tall with the flowers, with a spread of 3 feet. When grown in masses or used as a bedding plant, individual plants should be spaced approximately 30 inches apart. Its foliage tends to remain dense right to the ground, not requiring facer plants in front. It grows at a medium rate, and under ideal conditions can be expected to live for approximately 10 years.


HomeMiscCelebrating my 60th with wine, what else !
I recently celebrated my 60th birthday and judging by the number of wine-related cards and bottles I got I think, perhaps some is trying to tell me something. Not that I'm complaining as I still get enormous pleasure from receiving that special bottle of fizz or that sought after Rioja and I was very grateful for the generosity of family and friends.
This picture shows a small selection of some of the gifts I received and I'm very much looking forward to trying them. One of the bottles that stood out was the 'Vina Ardanza 2008' from an excellent producer 'La Rioja Alta'. Up to now, I've only tried this at tastings (although not the 2008) and I must say I was suitably impressed, so it was an easy choice to open up this as a birthday treat, amongst others.
Lots of bubbles consumed over the last week and a lot of it English, I'm becoming a big fan of Sparkling wine made here and I would put most of it up there with the better producers of Champagne.
If I had to describe this wine in one word it would be 'classy', it just oozes sophistication. From an excellent and reliable producer 'La Rioja Alta' comes this Rioja Reserva which is mostly Tempranillo with some Garnacha.
Now in its 75th year the Iconic 'Vina Ardanza' still stands proudly in the world of quality Rioja's.
This has an aroma to die for with vanilla, spice and sweet red fruits leaping from the glass which you could smell from the other side of the room.
Initially a silky mouthfeel with pronounced tannins, dark sweet cherry and raspberries followed by vanilla, cinnamon, tobacco and leather. This really was a joy to drink and all who tasted it gave it unanimous praise.
Priced at around the mid £20 mark I'd say its well worth the full price, I would definitely buy it and recommend that you all try it, even if you think you don't like Rioja, you may well be pleasantly surprised.
I can't thank family and friends enough for the fabulous selection of wines they bought for me, lots left to open and try, so keep a lookout for the next reviews there may well be my first 9/10 corks, the 'Vina Ardanza' was very close!!


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Home › Investment group talks about refocusing on environmental justice, racism
Investment group talks about refocusing on environmental justice, racism
Responsible Investing	 September 26, 2019
MRTI took criticism it was not engaging with communities to heart. Now that's core to its mission
by Rich Copley | Presbyterian News Service
The Rev. Dr. Gregory Simpson, at right, and the Rev. Kerri Allen of the Committee on Mission Responsibility Through Investment speak to the Presbyterian Mission Agency Board Thursday about efforts to better engage communities. (Photo by Rich Copley)
STONY POINT, New York — The members of the Committee on Mission Responsibility Through Investment (MRTI) heard the criticism: While they were making policy decisions about companies that were having a negative impact on the communities around them, the committee members were not engaging with those impacted communities.
"We had to confess that we were not doing that," the Rev. Kerri Allen, vice-chair of MRTI, said to the Presbyterian Mission Agency (PMA) board Thursday afternoon, the first day of its September meeting. "We went back to MRTI roots which had done that in the past, had been involved in this engagement with communities — where a lot of us who were newer members on the committee didn't realize that this was part of our history.
"And the leadership at MRTI that were looking forward said, 'That is a valid critique. We have to respond to that. We have to respond to what the General Assembly has said on environmental racism and take that seriously, and also take those voices seriously."
Thursday afternoon's talk, "Cultural Humility Training," took a look at MRTI's perspective on environmental racism and justice, now reflected in most aspects of its work, from the way it conducts quarterly meetings to its guideline metrics, scoring companies' environmental records.
"Environmental racism is now a central piece of what we do at MRTI," at-large committee member the Rev. Dr. Gregory Simpson said during the presentation. "We have to engage in this actual problem with some amount of forethought in every deliberation."
That started with looking at some terms that Simpson acknowledged can get a bit murky if you Google, say, "environmental justice."
In the talk, environmental justice aims to solve problems created by "the confluence of environmental destruction, racism, and poverty." The aim, Simpson said, is to make environmental protection more democratic, applying the political and ethical question, "Who gets what, why, and in what amount." They also had to define paternalism, for which they used Merriam-Webster's language, "a system under which an authority undertakes to supply needs or regulate conduct of those under its control in matters affecting them as individuals as well as in their relations to authority and to each other."
Environmental racism was defined as "the disproportionate burden of environmental problems that people of color take on," due to conditions such as substandard housing, living in less-desirable areas, and lacking the resources to get out.
Moving forward, the committee members said they were guided by Reformed theology as well as Presbyterian policy. The focuses on racism and poverty are also clearly in line with the goals of the Presbyterian Mission Agency's Matthew 25 invitation. MRTI's guideline metrics scores now include social impacts such as a company's effect on the neighborhoods surrounding its operations and record on human rights.
Rob Fohr, director of Faith-Based Investing and Corporate Engagement for the Presbyterian Church (U.S.A.), speaks to the Presbyterian Mission Agency Board at Stony Point Center Thursday. (Photo by Rich Copley)
And as the committee members and Rob Fohr, director of Faith-Based Investing and Corporate Engagement for the Presbyterian Church (U.S.A.) detailed, the committee engaged at two of its meetings this year, visiting with people impacted by climate change in New Orleans and the ravages of living near an oil refinery in Detroit.
Simpson and Allen told the board members tales of floating over stop signs and utility poles in communities around New Orleans and homes devalued down to a couple thousand dollars and residents suffering a wide variety of health impacts from living near a Marathon refinery in Detroit.
They weren't easy situations to be in and hear about, but committee members acknowledge hearing from impacted communities is vital.
"Each MRTI member has had to have an understanding of what's going on in the communities that we are talking about" Simpson said. "When you hear the problems and have to make decisions, that's not easy."
You may freely reuse and distribute this article in its entirety for non-commercial purposes in any medium. Please include author attribution, photography credits, and a link to the original article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDeratives 4.0 International License.
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Categories: Presbyterian Mission Agency, Responsible Investing
Tags: committee on mission responsibility through investment, community engagement, cultural humility training, environmental racism, faith-based investment and corporate engagement, gregory simpson, presbyterian mission agency board, rev. kerri allen, rob fohr
Tags: committee members, committee on mission responsibility, corporate engagement for the presbyterian, director of faith-based investing, engagement for the presbyterian church, environmental racism, faith-based investing and corporate, faith-based investing and corporate engagement, fohr director of faith-based, fohr director of faith-based investing, investing and corporate engagement, mission agency board, mission responsibility through investment, photo by rich copley, presbyterian church u.s.a, presbyterian mission, presbyterian mission agency, presbyterian mission agency board, rob fohr director, rob fohr director of faith-based
Ministries: Office of Faith-Based Investing and Corporate Engagement

redpajama_set_name: RedPajamaCommonCrawl


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Q My mother is terminally ill – she has one of those nasty neurological diseases that I can't pronounce. Bob, her husband of 21 years, has agreed to take her to Belgium (cheaper than Switzerland) so she may die in comfort and dignity. They want me on the trip. My only problem is, the law, being an ass, would view Bob and my actions as a crime. They point to Sarah Ninian as a precedent. I doubt we would face criminal prosecution. But how can we be sure Bob's and my inheritance would be protected from the forfeiture rule?
A. Houseman, Poole
A The bar bore, like his cousin occupied writing pulp fiction, would never tire of reminding us that prostitution isn't a crime. But working in brothel is.
By logic, suicide couldn't be a crime, if it were, what would be the penalty? However, assisting the suicide of another is a crime. A crime held so grave as to be worthy of a prison term of 14 years. Fourteen years. In the 300 years we've had the office of Prime Minister, the last PM to serve 14 years in office was Robert Jenkinson. His term ended in 1827.
The Forfeiture Rule
The forfeiture rule, whereby someone may no longer inherit, applies not only if the beneficiary is found guilty of killing the bequeather. The rule also applies if they are found to have assisted or encouraged that person's death.
Incentives matter.
One of several reasons adduced in opposition to decriminalising assisted suicide or assisted dying is the incentive for the avaricious to act not to end the suffering of the terminally ill, but to get their grubby hands on the estate of the ill person earlier than they might otherwise have by hastening their death. Or, horror upon horrors, the murderous would wear assisted suicide as a mask to do murder.
Of the case to which you refer: I recall this calamitous concatenation of events was headlined to highlight Mrs Sarah Ninian's supposed avarice – that she assisted her husband's suicide to get her hands on her husband's estate of one and four fifths of a million pounds.
Let's turn our backs on the clickbait headline.
Sarah Ninian…& The Forfeiture Rule
Alex Ninian, Sarah's spouse, terminally ill, journeyed to Switzerland to end his life. Mrs Ninian had to be won round to the notion of accompanying him on his final temporal journey – she had to be coaxed to make the travel arrangements.
The court ruled Sarah could inherit his estate despite her assisting in his suicide.
Mrs Ninian's success in the court rested on twin foundations. One, her initial opposition to his assisted dying – so her acts could not be construed as encouragement – and the Crown Prosecution Service's failure to bring a case against her. If on the day your performance in court fails to impress your audience, well, that's it. You have to be a sympathetic supplicant. That way you may enter the court with the optimism of a boxer entering the final rounds. Otherwise, the case will go against you. We'll do well to recall the QC
Courtenay Griffiths's contention from long ago: a court case is at bottom, a performance.
The redundancy of stating the obvious, that a court case is an expensive enterprise makes me feel foolish.
No Harm No Crime?
Even further beyond the headlines, Chief Master Matthew Marsh's judgement might be seen to cement a rule which states that when the criminal law brings no sanction, neither should the forfeiture rule. In support of this view, authority could be drawn from Lord-Justice Phillips' assertion: "Where the public interest requires no penal sanction, it seems to me that strong grounds are likely to exist for relieving the person who has committed the offence from all effect of the forfeiture rule."
[activecampaign]
At all times, despite Sarah never being prosecuted, never mind convicted. Nonetheless, she was referred to, sometimes obliquely and at other times to her face, as an offender.
The Assisted Dying Act is wending its way through Parliament. It's unlikely it will become law in this session of Parliament, or indeed any time soon.
At all times, you and I need to be able to laugh at suspicion. Therefore, while there are means and methods I could propagate, at the risk of appearing to assist an offender or conspire in the execution of an act currently against common law, especially as an officer of the court, I must beg to refuse to proffer an answer to your question.
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Guelph Connections Concerts Series
St. George's Church (map)
A solo piano recital given as part of the Guelph Connections Concerts Series at St. George's Church ·
Studio Series (Concerts@100)
A solo recital given as part of the Studio Series (Concerts@100). For tickets, please email Marko Duic at marko.duic@gmail.com
Arts and Letters Club of Toronto: Music Wednesday Recital
The Arts and Letters Club of Toronto (map)
A solo piano recital given as part of the 'Music Wednesday' concert series at the Arts and Letters Club of Toronto ·
Life and Music Concert Series
St. Andrew's Church (map)
A solo piano recital given as part of the 'Life and Music Noon Recitals concert series at St. Andrew's Church ·
Trinity-St. Paul's United Church: Lenten Concert Series
Trinity-St. Paul's United Church (map)
A solo piano recital given as part of the tripartite Lenten Concert Series at Trinity-St. Paul's United Church ·
Sunday Concert Series
Hope United Church (map)
A solo piano recital given as part of the Sunday afternoon concert series at Hope United Church ·
Guest Artist Recital
Natalie L.Haslam Music Center (map)
A solo piano recital given at the Natalie Haslam Music Center at the University of Tennessee's School of Music ·
Guest Artist Recital at the University of Virginia at Wise
University of Virginia at Wise (map)
A solo piano recital given at the at the University's Gilliam Center for the Arts ·
Nine Sparrows Arts Foundation
Yorkminster Park Baptist Church (map)
A solo piano recital given as part of the Nine Sparrows Arts Foundation's 'Lunchtime Chamber Music' concert series at Yorkminster Park Baptist Church ·
University of Toronto Thursday Noon Faculty Series
Faculty of Music, Edward Johnson Building (map)
Asher and violinist Emily Kruspe present an all-Brahms program of 16 Variations on a Theme by Robert Schumann, Op. 9 (piano solo), and Sonata for Violin and Piano No. 1 in G major, Op. 78 at the University of Toronto's Faculty of Music (Edward Johnson Building), in Walter Hall.
Noontime Recital
St Andrew's Church (map)
Asher and violinist Emily Kruspe present an all-Brahms program of 16 Variations on a Theme by Robert Schumann, Op. 9 (piano solo), and Sonata for Violin and Piano No. 1 in G major, Op. 78.
Outreach Concert at the Dotsa Bitove Wellness Academy
Dotsa Bitove Wellness Academy (map)
Asher and violinist Emily Kruspe present an all-Brahms program of 16 Variations on a Theme by Robert Schumann, Op. 9 (piano solo), and Sonata for Violin and Piano No. 1 in G major, Op. 78 at the Dotsa Bitove Wellness Academy.
Outreach Concert at the Centre for Addiction and Mental Health
CAMH (map)
Asher and violinist Emily Kruspe present the Sonata for Violin and Piano No. 1 in G major, Op. 78 at the Community Mall in the Centre for Addiction and Mental Health (1001 Queen W).
Lecture Recital at the University of Virginia at Wise
Asher will give a Lecture Recital "Relationships and Narratives of 'One Who Was Deprived of Tenderness': Brahms's 16 Variations on a Theme by Robert Schumann, Op. 9" at the University's Gilliam Center for the Arts.
Pax Christi Chorale: Romantic Masters
Grace Church on-the-Hill (map)
The inaugural concert of the Pax Christi Chorale's 2017-18 season, featuring Anton Bruckner's Te Deum, Johannes Brahms's Schicksalslied, and Ludwig van Beethoven's Choral Fantasy. Performers include Leslie Ann Bradley, soprano, Krisztina Szabó, mezzo-soprano, Isaiah Bell, tenor, Brett Polegato, bass-baritone, Asher Ian Armstrong, piano, the Toronto Mozart Players, with David Bowser, conductor.
Lecture Recital at the University of Northern Kentucky
Fine Arts Center, Greaves Concert Hall (map)
Asher will give the lecture "Structural Cyclicism and Narrative in César Franck's Prélude, Chorale, and Fugue" and a performance of the work at Northern Kentucky University's Fine Arts Center, in Greaves Concert Hall.
Edward Johnson Building (map)
Asher Ian Armstrong (piano) and Emily Kruspe (violin)
University of Tennessee School of Music (map)
Asher Ian Armstrong (piano) and Dr. Miroslav Hristov (violin)
Chamber Recital at St. Michael's College
McCarthy Arts Center, Recital Hall (map)
A joint recital program with cellist Jonathan Tortolano featuring solo and duo works for cello and piano, including J.S. Bach's 3rd Cello Suite, solo piano music of Liszt, the world premiere of Canadian composer Alexandra Delgado's Rhapsodie pour Violincelle et Piano, and the Cello Sonata in A major by César Franck.
Noon at Metropolitan Concert Series
Metropolitan United Church (map)
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck as part of the 'Noon at Met' concert series at Metropolitan United Church ·
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck as part of the 'Music Wednesday' concert series at the Arts and Letters Club of Toronto ·
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck as part of the Nine Sparrows Arts Foundation's 'Lunchtime Chamber Music' concert series at Yorkminster Park Baptist Church ·
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck as part of the 'Life and Music Noon Recitals concert series at St. Andrew's Church ·
Islington United Church (map)
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck as part of the 'Sundays at 3' concert series at Islington United Church ·
Guest Recital
Natalie Haslam Music Center (map)
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck at the University of Tennessee ·
Solo Piano Recital
Immaculate Conception Catholic Church (map)
A solo piano recital featuring transcriptions of J.S. Bach, Wagner, and works of Liszt and Franck at the Immaculate Conception Catholic Church in Knoxville, Tennessee, USA ·
Music For St. Valentine's Day
This program will include the Liebeslieder Walzer, Op. 52 of Johannes Brahms. Featuring singers of the Islington United Church Senior Choir and pianist Helen Becqué · Islington United Church, 25 Burnhamthorpe Road, Etobicoke, Ontario, M9A 1G9
Music For Good Friday: Motets by J.S. Bach, James MacMillian, Arvo Pärt
Featuring Trinity-St. Paul's United Church Choir, Sara Anne Churchill (organ), Elizabeth Loewen Andrews (violin), and Asher Armstrong (piano) · 427 Bloor St W, Toronto, ON M5S 1X7, Canada
Graduating Doctoral Recital
Edward Johnson Building, Faculty of Music (map)
Walter Hall · Edward Johnson Building · University of Toronto Faculty of Music · 80 Queens Park, Toronto, ON M5S 2C5 · Canada
Featuring the Prélude, Aria et Final of César Franck, the Vier Impromptus (D. 899) of Franz Schubert, and the Arabeske, Op. 18 and Études Symphoniques, Op. 13 of Robert Schumann ·


of the powers of observation.
I shall return to the hush.
have existed at the beginning.
for when the sun is quiet.
but the miracle is that she cares.
So many reasons not to.
down water pump, that's for sure.
never be that size again.
like ripples in a wave.
skinned hemlocks bled around us.


First I would do a test and see if the DCC suppy is OK to run the smoke unit.
Unfortunately I am reading this now. You should never have connected DCC current (AC and circa 15-16V) to the smoke generator. That is why you have function outputs on a decoder for. A decoder's function outputs can be used for this. If you don't have sound a function on your controller normally used for this could be used or in my case on British outline I don't use lights so I use the light function for my smoke generators.
I understand aces. You need to clearly know what the current and voltage requirements are of the smoke unit (I assume it is a Seuth or similar) and also know the function output voltage and current and tweak those using the appropriate values in the appropriate CVs of the decoder. Just hooking up things and seeing what happens can be costly.
It can be a pain to get all technical but that is what DCC is all about.
If you can tweak the values for voltage already just hook the function outputs up to a volt meter or mutimeter as it will be rectified DC currant and therefore measurable without any fancy tools. That way you can check that you have appropriate current and voltage entering the Seuth without a 4 hour drive.
Check too that the Seuth's original wiring or source of DC track current has been fully removed or isolated to ensure track voltage and current is not causing the over heating.
Did the smoke unit work fine before DCC?
Are you in Gippsland or the Western District?
Yeah I know the place used to day trip there as a kid and when my old man lived in Hamilton we would take the 4wd down that neck of the woods. I am a Geelong boy myself lost in the wild west!


First, Trump brought up the bombshell revelation – contained in a just-released undercover video – that Clinton-affiliated operatives actually paid and trained people (including homeless and mentally ill people) to attend the Trump rallies for the express purpose of provoking and inciting the very violence needed to create – out of whole cloth – the Clinton media-messaging narrative. Here was near-smoking-gun evidence that it happened, and that this dirty-trick tactic had been used to unfairly smear Trump and his supporters.
In the third and final presidential debate, Trump had what was seen by many to be his best debate performance on the issues. At the same time, Clinton arguably had her best debate performance as well, not so much on the issues, but with regard to her effective debate tactics of jabbing, pivoting, pandering, filibustering and keeping the focus on her campaign's very clear anti-Trump message. Trump then handed Clinton and the pro-Clinton media a hammer with which to pound him in the debate aftermath by not providing the politically correct-answer when asked if he would accept the outcome of the election. The net result was that the debate did more – in cold political terms – to reinforce Clinton's message rather than Trump's positions on the issues.
Campaigns often hinge on what is known in the trade as "message discipline" – the art of crafting one compelling and persuasive message and driving it home through enough repetition and examples that it becomes internalized in the body politic, nevermind whether or not it is true… or right… or even important in the larger scheme of things. Thus the big over-arching Clinton message this year – that Trump is somehow an unfit, unhinged, violence-prone, anti-American, anti-woman hate-monger – was contrived early on to gain political traction inside the knee-jerk left-leaning establishment-media and popular-culture echo-chamber. This message was designed in effect to become the fulcrum upon which team-Clinton wanted the election to be decided, to the virtual exclusion of all other domestic and foreign policy issues no matter how earth-shatteringly significant they might be for the future of the country.
One of the key pillars propping up this particular message was the notion that Trump had routinely incited and encouraged fisticuff violence against innocent peaceful first-amendment-loving protesters at his supposedly raucous rallies. It wasn't ever true. But the Clinton messaging about it was so effective that it was provided almost comic validation in one particular exchange at the debate.
But then a few moments later in her debate response, Clinton was able to blithely ignore her supporters having been caught red-handed in overseeing this sleazy and corrupt campaign tactic, while deriding Trump for "a pattern of divisiveness . . . where he incites violence, where he applauds people who are pushing and pulling and punching at his rallies. That is not who America is," she said.
And, virtually no one in the establishment media batted as much an eyelash. The anti-Trump message about him inciting violence against innocent peaceful protesters had so forcefully taken hold over time that its refutation by direct evidence was being treated largely as a non-story and a minor distraction. And, Clinton herself was probably considered to have effectively won points for actually "winning" this debate exchange…. and by extension for overseeing such a ruthlessly successful behind-the-scenes campaign tactic. It is a true window on the kind of manipulations that animate our current politics.
The further irony is that the same media types who can't get excited about the Clintons' proven exploitation of this – and possibly other – dark underbellies of the democratic process are just horrified to think that Trump was somehow undermining democracy itself by honestly refusing say he would automatically "accept" the "results" of the election. But what does this question even mean? Is he being asked to "accept" for example, the election-night pro-Clinton media "projections" – sight-unseen without all of the legitimate processes, including possible legal challenges, playing out? That is most likely how Trump quite reasonably could have interpreted the question.
And the further-further irony is that Clinton saying that she would "support our democracy" and quietly accept the results of the election if Trump won is no doubt one of two things: Either not essentially different from Trump's answer, or – knowing the Clintons – very close to being a transparent lie.


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Q: Android sort ArrayAdapter with exceptions for two rows I have an ArrayAdapter with Objects. Currently I sorted it with a compare-method. It works fine, but I have to switch to elements of the entries to the first and second row.
Can you help me by finding a good solution for this.
The List consists of Category names. "Last Used" and "favs" should be at the beginning.
Should I look for these entries after sorting, or would you do this at the same moment?

redpajama_set_name: RedPajamaStackExchange


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\section{Introduction}
\label{sec:intro}

Visual grounding~\cite{RefCOCO_and_REFCOCO+,RefCOCOg}, also known as Referring Expression Comprehension (REC), aims to predict the location of a region referred to by the language expression in an image. Previous solutions can be roughly divided into two-stage methods~\cite{19LearningToCompose,CMN,28LearnigToAssemble,46Learning2Branch,48neighbourhood,52dynamic,59mattnet,63grounding,68parallel} and one-stage methods~\cite{9real,27real,42zero,55improving,56fast}. The two-stage methods start with the process of generating region proposals via object detectors~\cite{FastRCNN} and then learning to identify the expected object from hundreds of candidates. On the other hand, the one-stage methods perform the grounding in an end-to-end manner, and often with inferior performances. However, the performance of these models is significantly limited due to the huge semantic gap between diverse referring descriptions and various visual appearances. The reason is that visual grounding needs to consider many open or fine-grained (\textit{e.g.}, {girl}, {boy}, {child}) categories, which is significantly different from the common vision tasks (\textit{e.g.}, classification, detection, and segmentation) where each image or individual object has a clear class label. Therefore, due to the diversity of descriptions in the human world, the model may easily overfit the descriptions in \textit{train} while hard to correctly understand the referring expressions in \textit{val} and \textit{test} when the training data is insufficient.

Recently, many researchers focus on using the attention mechanism in Transformer for Vision-Language (V-L) modeling \cite{VLBERT,ViLBERT,TransVG,MDETR}. With both visual and linguistic elements as the inputs, the Transformer encoder can perceive multi-modal data and thoroughly model the visual-linguistic relationship. Although these Transformer-based methods have achieved great success in vision-language modeling, they heavily rely on pre-training with extra large-scale vision-language data pairs to improve the generalization ability of the encoder and relieve the over-fitting issue, accordingly.

\begin{wrapfigure}[20]{r}{6cm
\centering
\includegraphics[width=1.0\linewidth]{Figure/Figure_intro_v2.pdf}
\caption{The sketch of our SiRi mechanism of three retraining periods. ``V'': Visual Backbone, ``L": Language Backbone, ``E'': Visual-Language Transformer Encoder, ``D": Transformer Decoder.
   The right part shows that we only take the last retrained model for the final test. Best viewed in color.}
\label{fig:onecol}
\end{wrapfigure}
However, without large-scale data pre-training, the model shows significant performance degradation on visual grounding tasks.
We observe that the relationship between the given expression and the image perceived by the Transformer encoder leaves much to be desired based on the poor V-L interaction attention map in Fig.~\ref{fig:onecol}.
The reason may be that the Transformer encoder, started with randomly initialized parameters, may easily over-fit a small number of training pairs and make the model be trapped into a poor local minimum. With such an observation, we raise the question of \emph{whether the V-L model will converge to a better local minimum by equipping the Transformer encoder with better-initialized parameters?}

To answer the above question, in this paper, we investigate a new training mechanism to improve the Transformer encoder, named \textbf{S}elect\textbf{i}ve \textbf{R}etra\textbf{i}ning (SiRi), which repeatedly reactivates the learning of the encoder in the process of continuous retraining and progressively provide better-initialized parameters for the encoder in the next stage.
Specifically, while we \textit{continually update} parameters of the encoder as the training goes on, we \textit{periodically re-initialize} all the other modules (\textit{e.g.}, vision/language backbones and the Transformer decoder). In this way, the SiRi promotes the encoder to continually learn better vision-language relationships by periodically getting out of the sub-optimal saddle point. Fig.~\ref{fig:onecol} shows the sketch of SiRi and the visualization of the encoder's attention weight after each retraining period, where we can clearly see the progress of the encoder in multi-modal modeling.

We conduct extensive experiments to validate the effectiveness of our method.
With the proposed SiRi mechanism, our model remarkably outperforms previous approaches on three popular benchmarks. Particularly, we achieve 83.04\% at top-1 accuracy on RefCOCO+ \emph{testA}~\cite{RefCOCO_and_REFCOCO+}, outperforming the state-of-the-art approaches by more than 10.21\%.

More importantly, we further observe that the SiRi mechanism helps model generalize well to small-scale training data as shown in Fig.~\ref{fig:bar_chart} (d). To be specific, our model with a quarter of training data outperforms previous state-of-the-art methods (with full training data) by 1.65\% on the RefCOCOg \textit{val} set. With even less training data (\textit{e.g.}, only 10\%), we almost double the accuracy (61.58\% \textit{versus} 32.00\%) compared to the baseline. Additionally, we complement more extensibility studies in other visual grounding model and other V-L tasks related to visual grounding. We found SiRi can further improve the top-1 accuracy by an average of 2\% in TransVG~\cite{TransVG}, which is also a Transformer-based visual grounding model. We visualize the improvement of different model with SiRi on three datasets in Fig.~\ref{fig:bar_chart} (a) - (c). In other V-L tasks, including referring expression segmentation, phrase grounding, and visual question answering tasks, we can also improve the baseline using the SiRi mechanism.

\begin{figure}[t]
  \centering
    \includegraphics[width=1.00\linewidth]{Figure/Figure_bar_chart.pdf}

  \caption{(a)-(c) illustrates the performance enhancement of SiRi on MDETR~\cite{MDETR} and TransVG~\cite{TransVG}. We test on three popular visual grounding datasets RefCOCO, RefCOCO+, RefCOCOg. (d) shows that when training with 10\%, 25\%, 50\%, 100\% \emph{training} data, the top1 accuracy improvement of SiRi on the RefCOCOg \emph{validation} set.
  }
  \label{fig:bar_chart}
\end{figure}

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

\subsection{Visual Grounding}
Existing methods for Visual Grounding based on CNN can be roughly divided into two categories, namely two-stage methods and one-stage methods.

\noindent \textbf{Two-stage methods}~\cite{19LearningToCompose,CMN,liang2021clawcranenet,liang2021rethinking,28LearnigToAssemble,46Learning2Branch,48neighbourhood,wu2022snoc,52dynamic,59mattnet,63grounding,68parallel} typically utilize an object detector to generate region proposals in the first stage, and then find the best matched region-text pair. The object-text pair matching is commonly used in visual grounding task and other V-L tasks, \textit{e.g.}, retrieval tasks~\cite{HGA}. MattNet~\cite{59mattnet} takes a modular approach to progressively understand and unify visual and linguistic semantic information in terms of attributes, relationships, and location. Additionally, some approaches further enhance the modeling ability of multi-modal relations using graph structures~\cite{48neighbourhood,52dynamic,53graph}, multi-modal tree structures~\cite{28LearnigToAssemble}.

\noindent
\textbf{One-stage methods}~\cite{9real,27real,42zero,55improving,56fast} avoid being constrained by the quality of the proposal by directly fusing visual and linguistic features.
FAOA~\cite{56fast} represents the text input with a language vector and leverages it into the YOLOv3 detector~\cite{40yolov3} to align the referred instance. RCCF~\cite{27real} regards the visual grounding problem as a correlation filtering process~\cite{4visual,17high}, and the peak value in the correlation heatmap is selected as the center of target objects. In ReSC~\cite{55improving}, the limitation of FAOA~\cite{56fast} on grounding complex queries is broken through with a recursive sub-query construction module.

In the previous CNN-based visual grounding model, the V-L fusion is performed throughout the decoding process, which is weak interpretability and performance compared to the V-L fusion module in Transformer-based model. Therefore, we adopt Transformer-based model for better V-L interaction.

\subsection{Transformer-based Methods in REC}
Recently, Transformer~\cite{45Transformer} has been widely used to address the multi-modal semantic alignment problem.
However, Transformer is data-hungry and thus usually needs additional large-scale pretraining.
Motivated by the excellent performance of BERT~\cite{12bert}, some researchers~\cite{VLBERT,UNITER,ViLBERT,ERNIE-ViL,VILLA,ViLT,LXMERT} construct similar structures and propose multi-modal pre-training for Visual-Language Pre-training (VLP) tasks. These approaches introduce pretext tasks for better interaction of vision and language, \textit{e.g.}, masked language modeling~\cite{ViLBERT,VLBERT}, image-text matching~\cite{ViLT}.
However, these VLP methods usually require pre-training with large-scale data and fine-tuning on downstream tasks to achieve good results.
Recently, TransVG~\cite{TransVG} study the Transformer-based framework without pretraining.
Without extracting region proposals in advance, TransVG directly regresses bounding box coordinates and predicts the referring objects.

These works have validated the effectiveness of Transformer for multimodal modeling. However, most of them require large-scale data to pretrain a Transformer-based model.
Differently, in this work, we focus on exploring a way to train better encoders \textit{without} large-scale pretraining.

\subsection{Re-training}

Some early works avoid getting trapped in a local minimum by introducing randomness. For example, ensemble learning~\cite{NN_Ensembles_1,NN_Ensembles_2} introduces randomness by retraining the model with different random initialized parameters to converge to different local minimums. %
Due to these studies requiring an overwhelming cost, a number of retraining methods, \textit{e.g.}, Dropout~\cite{dropout}, Distillation~\cite{distilling}, are proposed to reduce the cost of retraining in ensemble learning. More recently, Snapshot Ensemble~\cite{Snapshot} proposes to retrain the same model to access multiple local minimums by the cyclic learning rate. Similarly, the cyclic learning rate is used in the retraining process to detect noisy labels in O2U-Net~\cite{o2u-net}. However, Transformer~\cite{45Transformer} is very sensitive to the learning rate and sometimes requires a warm-up or inverse square root learning rate, which makes the cyclic learning rate~\cite{cycle_lr} inapplicable. The proposed weight initialization scheme T-Fixup in~\cite{Transformer_better_initial} enables Transformer training without warmup or layer normalization. Han~\emph{et al.}~\cite{DSD} proposes DSD retraining mechanism with reference to the model pruning, which avoids over-fitting caused by over-capturing of noisy data.

The SiRi mechanism proposed in this paper is somehow similar to the above methods but SiRi is designed for the V-L fusion module in V-L tasks. The main motivation of re-training in this paper is to provide the V-L fusion Transformer with better-initialized parameters.

\section{Method}
\label{sec:method}

In this section, we first briefly review the basic visual grounding architecture adopted by this work in Sec.~\ref{subsec:base arch.}. Then we elaborate on our proposed SiRi mechanism in Sec.~\ref{subsec:SiRi} and the Multi-task SiRi in Sec.~\ref{subsec:dual decoder}.

\subsection{Base Architecture}
\label{subsec:base arch.}
We follow the state-of-the-art model MDETR~\cite{MDETR} as our base architecture, which consists of four main modules: (1) Visual Backbone; (2) Language Backbone; (3) Visual-Language Transformer Encoder; (4) Transformer Decoder Module.

\noindent \textbf{Visual Backbone $\mathcal{V}$} \& \textbf{Language Backbone $\mathcal{L}$.}
We adopt the convolutional backbone ResNet-101~\cite{ResNet} to obtain the visual representation for an input image ${\mathbf{I}}$.
In previous work MDETR~\cite{MDETR}, they only take the output of the last CNN stage as visual features.
Differently, we believe the features of shallow stages (\textit{e.g.}, the third stage in ResNet-101) benefit localizing objects if the sentence contains a detailed low-level description such as color.
Therefore, we take the output of the third stage of ResNet-101 and transform it with two dilated convolution layers.
Then we add the adjusted dimensionality low-level feature together using the final-stage output of ResNet-101 as the final visual representations.
Then we encode referring expressions with the pretrained language model RoBERTa~\cite{RoBERTa}.

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=1.00\linewidth]{Figure/Figure_siri_stage.pdf}
   \caption{The training process of our SiRi mechanism. The parameters of the module with solid color background are initialized as the original rules, while those with slash background are trained. The base architecture contains four main modules: (1) ``Vis.": Visual Backbone; (2) ``Lan.": Language Backbone; (3) ``V-L Encoder": Visual-Language Transformer Encoder; (4) ``Decoder Module": Transformer Decoder Module.}

\label{fig:arch}
\end{center}
\end{figure*}

\noindent \textbf{Visual-Language Transformer Encoder $\mathcal{E}$.}
We use a Transformer~\cite{45Transformer} as the encoder for vision-language interaction, where the model performs the cross-modal fusion and association.
To do so, we flatten the visual features and add 2-D positional embeddings to conserve spatial information.
After that, we project both the flattened visual features and text features into a shared embedding space and then concatenate them into a single sequence of image and text features.
The sequence is then input to the cross encoder Transformer for further visual-language interaction.

\noindent \textbf{Transformer Decoder $\mathcal{D}$.} Following DETR~\cite{DETR}, we use a Transformer decoder to predict the target bounding boxes.
The decoder takes as input a set of learnable object queries, cross-attends to the encoder output and predicts embeddings for each query.
After that, we decode the embeddings into box coordinates and class labels by the regression and classification heads.
Considering that the number of relevant referred targets is fewer than the total number of objects of an image, we limit the decoder to have 16 query inputs only.
Considering there is only sentence-level correspondence in visual grounding, we remove box-token contrastive alignment loss~\cite{MDETR}.
Accordingly, we also reduce the length of the soft tokens to 2, standing for whether the object box belongs to the expression.

\subsection{SiRi: Selective Retraining Mechanism}
\label{subsec:SiRi}

\begin{wrapfigure}[15]{r}{6cm
\centering
\includegraphics[width=0.9\linewidth]{Figure/rebuttal_loss_curve.pdf}

\caption{The train and test loss curves in \textit{Initial train} stage and \textit{Retrain} stage. }
\label{fig:curve}
\end{wrapfigure}

The transformer model may easily get over-fitted without large-scaled pretraining.
As shown in Fig.~\ref{fig:curve}, the test loss increases even though the training loss still declines after point A of the initial training stage.
Simply having more training iterations would not further improve the test performance.

Motivated by our hypothesis that a V-L model may converge to a better local minimum by equipping the Transformer encoder with better initialized parameters, we design the Selective Retraining (SiRi) mechanism.
After the initial training, we continually update the parameters of the encoder as the training goes on, while periodically re-initializing the parameters of the decoder to compel the model to be better optimized based on an enhanced encoder. By applying our SiRi mechanism at point B in Fig.~\ref{fig:curve}, both {training loss} and {test loss} further decline, thus we obtain better optimization results (lower test loss).
To be specific, our Selective Retraining Mechanism is set up as follows.

\textbf{Initial Training.}
We initialize the visual Backbone $\mathcal{V}$  and the language Backbone $\mathcal{L}$ using the ResNet-101~\cite{ResNet} model pre-trained from ImageNet~\cite{ImageNet} and the RoBERTa model pre-trained from language corpus datasets, respectively.

The rest of our model (\textit{e.g.}, Transformer encoder and decoder) are randomly initialized using the Xavier initialization~\cite{Xavier}.
We denote the initialized parameters of the Visual Backbone together with the visual linear projection layer as $ {\mathcal{V}_{0}}$, and Language Backbone together with the corresponding linear projection layer as $ {\mathcal{L}_{0}}$.
Similarly, the model weights of Transformer Encoder and Transformer Decoder are denoted as $ \mathcal{E}_{0}$ and $ {\mathcal{D}_{0}}$, respectively.
We then train the model using a combination of the object coordinates regression losses (L1 \& GIoU) and soft-token prediction loss (cross-entropy loss) while keeping the learning rate unchanged.
The model training stops when the validation performance stays stable.
We denote the trained model weights to be $ {\mathcal{V}_{0}^{'} },  {\mathcal{L}_{0}^{'} },  {\mathcal{E}_{0}^{'} },  {\mathcal{D}_{0}^{'} }$ after the initial training.

\textbf{Selective Retraining.}
To further improve the encoder with better vision-language understanding, we continually train the encoder after the initial training, while \textit{re-initialize} the other modules to avoid getting stuck in local minimums. We show the pipeline of SiRi in Fig.~\ref{fig:arch}.
Specifically, for the $t$-th round of the selective retraining, we only keep the encoder $\mathcal{E}_{t}$ to be up-to-date, \textit{i.e.}, $\mathcal{E}_{t} \gets \mathcal{E}_{t-1}^{'}$, where $\mathcal{E}_{t-1}^{'}$ is the previous trained encoder from $t-1$ round.
As for other modules including the decoder $\mathcal{D}_{t} $, the visual backbone $\mathcal{V}_{t}$, and the language backbone $\mathcal{L}$, we drop the trained weights and re-initialize them using their original initialization at the initial training stage, \textit{i.e.}, either initializing from the pre-trained weights (\textit{e.g.}, $\mathcal{V}_0$ and $\mathcal{L}_0$), or random initialization (\textit{e.g.}, the decoder $D$).
We then re-train the whole model using the same learning rate until it converges.

\subsection{Multi-task SiRi}
\label{subsec:dual decoder}

As a common practice for transformer models, multi-task learning usually benefits the model optimization and thus alleviates over-fitting issues.
Therefore, we further extend SiRi to a multi-task version by incorporating an auxiliary decoder.
Specifically, we use two diverse decoders to generate predictions based on the same encoder output and then optimize the encoder using the two decoder losses.

To ensure the two decoders are different from each other, we design two different object queries (positional embeddings) for decoders.
Previous DETR~\cite{DETR} uses \textit{learnable} positional embeddings as the object query to attend to the encoder output.
Differently, we adopt a {\textit{constant}} positional encoding sequence, \textit{i.e.}, the sine-cosine position encoding function, to generate the object queries for the other decoder.
The two decoders take different queries to attend to the same encoder output, which would urge the encoder to be more robust in vision-language interaction.
The details are shown in Appendix~\ref{subsec:dual decoder details}.

\section{Experiments}

\subsection{Datasets}
\label{sec:data}

\textbf{RefCOCO/RefCOCO+ } are proposed in ~\cite{RefCOCO_and_REFCOCO+}. There are 19,994 images in RefCOCO  with 142,209 refer expressions for 50,000 objects. Similarly, 19,992 images are included in RefCOCO+ which contains 141,564 expressions for 49,856 objects. In these datasets, each image contains two or more objects from the same category. In RefCOCO+ dataset, positional words are not allowed in the referring expression, which is a pure dataset with appearance-based referring expression, whereas RefCOCO imposes no restriction on the phrase.
In addition to the training set and validation set, the test set for RefCOCO/RefCOCO+ is divided into a \textit{testA} set (containing several people in an image) and a \textit{testB} set (containing multiple instances of other objects in an image).

\textbf{RefCOCOg}~\cite{RefCOCOg} contains 26,711 images with 85,474 referring expressions for 54,822 objects, and each image usually contains 2-4 objects of the same category. The length of referring expressions in this dataset is almost twice as long as those in RefCOCO and RefCOCO+.

\begin{table}[!t]
\centering
\small
\caption{Comparisons with state-of-the-art methods on RefCOCO~\cite{RefCOCO_and_REFCOCO+}, RefCOCO+~\cite{RefCOCO_and_REFCOCO+}, and RefCOCOg~\cite{RefCOCOg} in terms of top-1 accuracy. We also report official MDETR implementation~\cite{MDETR} without pretraining (denoted as MDETR w/o pretrain) and our improved MDETR implementation (see Sec.~\ref{subsec:base arch.}) (denoted as MDETR*).  ``MT SiRi" means ``Multi-task SiRi". }
\scalebox{0.78}{
\begin{tabular}{c|c|c|ccc|ccc|cc}
\toprule
\multirow{2}{*}{Method}                          & \multirow{2}{*}{Venue} & \multirow{2}{*}{Visual backbone} & \multicolumn{3}{c|}{RefCOCO}                                                       & \multicolumn{3}{c|}{RefCOCO+}                                                      & \multicolumn{2}{c}{RefCOCOg}                           \\
                                                 &                        &                                  & val                       & testA                     & testB                      & val                       & testA                     & testB                      & val                       & test                       \\
\hline\hline
\textit{CNN-based:}                              & \multicolumn{1}{l|}{}  & \multicolumn{1}{l|}{}            & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}       \\
CMN~\cite{CMN}                                             & CVPR'17                & VGG16~\cite{VGG}                           & -                         & 71.03                     & 65.77                      & -                         & 54.32                     & 47.76                      & -                         & -                          \\
MAttNet~\cite{59mattnet}                                         & CVPR'18                & ResNet-101~\cite{ResNet}                       & 76.65                     & 81.14                     & 69.99                      & 65.33                     & 71.62                     & 56.02                      & 66.58                     & 67.27                      \\
RvG-Tree~\cite{19LearningToCompose}                                        & TPAMI'19               & ResNet-101                       & 75.06                     & 78.61                     & 69.85                      & 63.51                     & 67.45                     & 56.66                      & 66.95                     & 66.51                      \\
NMTree~\cite{28LearnigToAssemble}                                          & ICCV'19                & ResNet-101                       & 76.41                     & 81.21                     & 70.09                      & 66.46                     & 76.02                     & 57.52                      & 65.87                     & 66.44                      \\
FAOA~\cite{56fast}                                            & ICCV'19                & DarkNet-53~\cite{40yolov3}                      & 72.54                     & 74.35                     & 68.50                      & 56.81                     & 60.23                     & 49.60                      & 61.33                     & 60.36                      \\
RCCF~\cite{27real}                                            & CVPR'20        & DLA-34~\cite{DLA}                          & -                  & 81.06                     & 71.85                      & -                         & 70.35                     & 56.32                      & -                         & 65.73                      \\
MCN~\cite{MCN}                                             & CVPR'20                & DarkNet-53                       & 80.08                     & 82.29                     & 74.98                      & 67.16                     & 72.86                     & 57.31                      & 66.46                     & 66.01                      \\
ReSC-Large~\cite{55improving}                                      & ECCV'20                & DarkNet-53                       & 77.63                     & 80.45                     & 72.30                      & 63.59                     & 68.36                     & 56.81                      & 67.30                     & 67.20                      \\
\hline\hline
\textit{Transformer-based}                       & \multicolumn{1}{l|}{}  & \multicolumn{1}{l|}{}            & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}       \\
\textit{Pretrained:}                             & \multicolumn{1}{l|}{}  & \multicolumn{1}{l|}{}            & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}       \\
ViLBERT~\cite{ViLBERT}                                         & NeurIPS'19                & ResNet-101                       & -                         & -                         & -                          & 72.34                     & 78.52                     & 62.61                      & -                         & -                          \\
ERNIE-ViL~\cite{ERNIE-ViL}                                      & AAAI'20                & ResNet-101                       & -                         & -                         & -                          & 75.95                     & 82.07                     & 66.88                      & -                         & -                          \\
UNTIER~\cite{UNITER}                                         & ECCV'20                & ResNet-101                       & 81.41                     & 87.04                     & 74.17                      & 75.90                     & 81.45                     & 66.70                      & 74.86                     & 75.77                      \\
VILLA~\cite{VILLA}                                          & NeurIPS'20                & ResNet-101                       & 82.39                     & 87.48                     & 74.84                      & 76.17                     & 81.54                     & 66.84                      & 76.18                     & 76.71                      \\
MDETR~\cite{MDETR}                                           & ICCV'21                & ResNet-101                       & \multicolumn{1}{l}{86.75}           & \multicolumn{1}{l}{89.58} & \multicolumn{1}{l|}{81.41}           & \multicolumn{1}{l}{79.52} & \multicolumn{1}{l}{84.09} & \multicolumn{1}{l|}{70.62} & \multicolumn{1}{l}{81.64} & \multicolumn{1}{l}{80.89} \\
\hline\hline
\multicolumn{1}{l|}{\textit{~~~Transformer-based} } & \multicolumn{1}{l|}{}  & \multicolumn{1}{l|}{}            & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}       \\
\textit{without Pretrained:}                     & \multicolumn{1}{l|}{}  & \multicolumn{1}{l|}{}            & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l|}{}      & \multicolumn{1}{l}{}      & \multicolumn{1}{l}{}       \\
TransVG~\cite{TransVG}                                         & ICCV'21                & ResNet-101                       & 81.02                     & 82.72                     & 78.35                      & 64.82                     & 70.70                     & 56.94                      & 68.67                     & 67.73                      \\
MDETR (w/o pretrain)~                                               & ICCV'21                & ResNet-101                       & 78.01                     & 82.18                     & 72.56                                & 68.01                     & 72.83                     & 55.57                      & 65.54                     & 65.99                      \\
MDETR*                                                              & -                      & ResNet-101                       & \multicolumn{1}{l}{81.49} & \multicolumn{1}{l}{84.67} & \multicolumn{1}{l|}{76.58}           & \multicolumn{1}{l}{70.93} & \multicolumn{1}{l}{75.65} & \multicolumn{1}{l|}{59.27} & \multicolumn{1}{l}{69.59} & \multicolumn{1}{l}{70.22}  \\
\textbf{MDETR* + SiRi}                                                & -                      & ResNet-101                       & \multicolumn{1}{l}{\textbf{85.83}} & \multicolumn{1}{l}{88.56} & \multicolumn{1}{l|}{\textbf{81.27} } & \multicolumn{1}{l}{76.68} & \multicolumn{1}{l}{82.01} & \multicolumn{1}{l|}{66.33} & \multicolumn{1}{l}{76.63} & \multicolumn{1}{l}{76.46}  \\
\textbf{MDETR* + MT SiRi}                                             & -                      & ResNet-101                       & 85.82            & \textbf{89.11}            & 81.08                                & \textbf{77.47}            & \textbf{83.04}            & \textbf{67.11}             & \textbf{77.39}            & \textbf{76.80}             \\
\bottomrule
\end{tabular}
}

\label{tab:sota_compare}
\end{table}

\subsection{Experimental Settings}
\label{sec:expset}

\textbf{Implementation Details.}
Following MDETR~\cite{MDETR}, all parameters in the network are optimized using AdamW~\cite{AdamW} with the learning rate warm-up strategy.
The model is trained using 4 GPUs with a batch size of 72.
We set the learning rate of the language backbone RoBERTa~\cite{RoBERTa} to be $1\times10^{-5}$, and all the rest parameters to be $5\times10^{-5}$.
In initial training, the model with a single decoder is trained for 55 epochs, and the model with a dual decoder (multi-task SiRi) is trained for 35 epochs since it converges quickly. Each retraining stage takes another 30 training epochs.
We set the maximum side length of the input image as 640 while keeping the original aspect ratio. Images in the same batch are padded with zeros until acquiring the largest size of that batch. Similarly, sentences in one batch will be adjusted to the same length as well.
We continually retrain the model until the validation performance converges (usually 5 to 8 rounds).

\textbf{Evaluation Metrics. }
Following the proposal setting in the previous work, we use the metric Prec@0.5 to evaluate our method, where a predicted region will be regarded as a positive sample if its intersection over union (IoU) with the ground-truth bounding box is greater than 0.5.

\subsection{Comparison with State-of-the-art Methods}
\label{sec:sota}

We compare our method with other state-of-the-art methods on three common benchmarks of Referring Expression Comprehension, \textit{i.e.}, RefCOCO, RefCOCO+, and RefCOCOg.
Results are reported in Table~\ref{tab:sota_compare}.  Our method displays significant improvement over previous methods on all three datasets.
Compared to models without large-scale pretraining, which is a fair comparison, we outperform them by more than 6.39\% on RefCOCO@testA, 10.21\% on RefCOCO+@testA, and 9.07\% on  RefCOCOg@test.
Even compared to those large-scaled pretrained models, \textit{e.g.}, MDETR pretrained using more than one million aligned image-text pairs, our method still achieves comparable results on RefCOCO without those extra data.

\subsection{Ablation Studies}
\label{sec:ablation}

\textbf{Different Retraining Module.}
Besides continually updating the encoder while periodically re-initializing all the other parts, we also evaluate different re-initializing modules.
\begin{figure*}[!t]
  \begin{minipage}[b]{0.70\textwidth}
        \begin{center}
        \includegraphics[width=0.99\linewidth]{Figure/Figure_experiment.pdf}

        \end{center}
          \caption{Schematic of the eight retraining variants with different combinations of selective modules. The solid color background means re-initializing parameters, while the slash background means continually updated parameters from previous periods. Best viewed in color. }
        \label{fig:retrain mode}
  \end{minipage
  \hspace{0.01\textwidth}
  \begin{minipage}[b]{0.28\textwidth}
    \centering
    \small

    \scalebox{0.75}{
    \begin{tabular}{l|c}
    \toprule
    \multicolumn{1}{c|}{Mode} & \multicolumn{1}{c}{RefCOCO+@val}  \\

    \hline\hline
    \multicolumn{1}{c|}{Initial}              & 71.45        \\
    \hline
    (a) V,L,E,D                                & 71.98         \\
    (b) V,L,E                                  & 72.12         \\
    (c) V,D                                    & 72.14         \\
    (d) V,E                                    & 73.25          \\
    (e) L,E                                    & 73.44          \\
    (f) E,D                                    & 73.80          \\
    (g) D                                      & 72.76          \\
    (h) E                                      & \textbf{74.14}         \\
    \bottomrule
    \end{tabular}}
    \makeatletter\def\@captype{table}\makeatother
        \setlength{\abovecaptionskip}{5mm}

        \caption{Performance comparison of different selective modules. The eight mode are shown in Fig.~\ref{fig:retrain mode}.}

    \label{tab:retrain}
  \end{minipage}
\end{figure*}

We show eight variants of our SiRi Mechanism in Fig.~\ref{fig:retrain mode},
For a fair comparison, we keep all hyperparameters the same and retrain these variants from the same initial trained model.
We show their correspondence results after the first retraining in Table~\ref{tab:retrain}.
The encoder with better initialized parameters is the critical factor for the  whole model converging to a better local minimum.

Comparing mode (d) with mode (h), we find that re-initializing the \textit{visual} backbone has great impact on performance boosting, which verifies our motivation that re-initializing the input of encoder helps to get out of local minimums while keeping the essential cross-modeling ability of previous models.
Similar results can be found for \textit{language} backbone by comparing mode (e) with mode (h).
Interestingly, we find that the performance is competitive to Mode (h) when we use Mode (f), where we keep the parameters of both encoder and decoder.
For simplicity, we only keep the encoder updated continually in all the other experiments.

\textbf{Retraining Periods.}
In Fig.~\ref{fig:retrain_times}, we show the validation performance curves during selective retraining.
Zero indicates the initial trained model in the figure.
We can see the model performance increases a lot in the first three retraining periods and then tends to converge after several retraining periods.
The highest performances are achieved in the fifth retraining period, where SiRi outperforms the initial trained model by 5.18\% (72.29\% versus 77.47\%) and 5.86\% (71.53\% versus 77.39\%) on RefCOCO+ and RefCOCOg, respectively.

\textbf{Different Object Queries in Multi-task SiRi.} We can also see the consistent performance gap between the single SiRi and the multi-task SiRi in Fig.~\ref{fig:retrain_times}.
The multi-task SiRi always performs better than single SiRi during all the retraining periods.
We further study the impact of different object queries (\textit{e.g.}, learnable queries and constant queries) used in Multi-task SiRi.
The results of the initial trained models using different quires in multi-task learning are shown in Table~\ref{tab:query_set}.

Although learnable and constant object queries achieve similar results for single task training, the combination of them in multi-task learning achieves higher performance (72.29\% \textit{versus} 70.93\% on RefCOCO+).
Note that multi-task structure with two identical object query types (\textit{e.g.}, both learnable or both constant) does not outperform single task learning.
It indicates that taking different queries to attend the same encoder output may help the encoder to be more robust on vision-language interaction.

\begin{figure*}[!t]
  \begin{minipage}[b]{0.66\textwidth}
        \begin{center}
        \includegraphics[width=0.99\linewidth]{Figure/Figure_mtsiri.pdf}

        \end{center}
          \caption{Performance achieved by increasing the training periods. The blue line indicates the single SiRi model and the red line indicates the multi-task SiRi model. ``MT" indicates multi-task.}
          \label{fig:retrain_times}
  \end{minipage
  \hspace{0.01\textwidth}
  \begin{minipage}[b]{0.32\textwidth}
    \centering
    \small

    \scalebox{0.67}{

    \begin{tabular}{c|cc|c}
    \toprule
    \multirow{2}{*}{Structure}      & \multicolumn{2}{c|}{Object Queries} & \multirow{2}{*}{RefCOCO+}  \\
                                    & \textit{1st} Dec.  & \textit{2nd} Dec.                 &                                \\
    \hline\hline
    \multirow{2}{*}{Single-task} & L & –                        & 70.93                          \\
                                    & C  & –                        & 70.72                          \\
    \hline
    \multirow{3}{*}{Multi-task}   & L & L                & 70.27                          \\
                                    & C  & C                 & 71.24                          \\
                                    & L & C                 & \textbf{72.29}                          \\
    \bottomrule
    \end{tabular}}
    \makeatletter\def\@captype{table}\makeatother %
        \setlength{\abovecaptionskip}{6mm}
        \caption{Ablation studies on different object query types in multi-task SiRi. (``L": learnable queries, and ``C": constant queries, ``Dec.": Decoder.)}
    \label{tab:query_set}
  \end{minipage}
\end{figure*}

\subsection{Qualitative Results}
\label{sec:visualization}

We visualize the attention weight of encoders along with the retraining progress in Fig.~\ref{fig:attention visual}.
To be specific, we calculate the cross-modal attention weights (vision output tokens based on language input tokens) from the last layer of the Transformer encoder, and then visualize them in the original image size.
We believe the values of cross-modal attention weights indicate the encoder's ability of vision-language understanding.

We show two test samples in the figure with the corresponding input sentences.
From left to right, we show the bounding box predictions together with the attention maps generated by the initial trained, 1st, 3rd, 5th, and 7th retrained encoders, respectively.
It can be intuitively seen that the encode learns to better perceive the relationship between expressions and images as the continuous SiRi training goes.
Taking the upper sample as an example, the predicted bounding box is incorrect from the initial trained model, where we can see the attention map of the first encoder does not highlight the referred object, either.

After selective retraining, the encoder gets better and better, which can be seen from the more accurate attention maps.
Therefore, the predicted boxes are also better than the initial ones.
It validates our motivation that the better encoder initialization helps the model converge to a better local minimum.
Continually updating the encoder while periodically re-initializing other modules can strengthen the visual-linguistic modeling.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=0.95\linewidth]{Figure/figure7_v3.pdf}
\end{center}
   \caption{Visualization of the predicted box and the encoder's cross-modal attention weights in inference. The columns represent initial trained, 1st retrained, 3rd retrained, 5th retrained, 7th retrained model, respectively, from left to right. As we can see, the model prediction gets better as the encoder attention map gets clear.
   }
\label{fig:attention visual
\end{figure*}

\subsection{Extensibility Studies}
\label{sec:extensibility}

To better show the generality, we further extend SiRi to more visual grounding settings, models, and tasks.

\textbf{Extend to Small Data Size.}
First, we study how SiRi performs with fewer training data, where the over-fitting issue is more severe. To do so, we randomly sample 10\%, 25\%, and 50\% of training data from the RefCOCOg training set as the new training splits, respectively.
Then we train the model following the SiRi mechanism\footnote{We train more epochs until converging in small-scale experiments.} and then evaluate the performance on the full validation set of RefCOCOg (the same validation set for all).
The results are shown in Fig.~\ref{fig:datasets_scale}.
\begin{wrapfigure}[17]{r}{6cm
\centering
\includegraphics[width=0.95\linewidth]{Figure/Figure_dataset_scale.pdf}
\caption{Performance improvement of the model with SiRi with limited training samples. We randomly sample 10\%, 25\%, 50\% of training data from RefCOCOg and train with SiRi. All models are evaluated on the same RefCOCOg \textit{val} set. }
\label{fig:datasets_scale}
\end{wrapfigure}
Compared with the initial trained model, our SiRi model shows very impressive performance gains, \textit{e.g.}, almost doubling the performance at 10\% sampling rate.

As can be seen from the figure, the performance is improved much more significantly when employing the SiRi mechanism on fewer training data, which verifies that our SiRi can generalize the vision-language encoder and avoid over-fitting.
It suggests that our SiRi mechanism may be potentially treated as a strong alternative to large-scale pre-training models.

\noindent \textbf{Extend to other V-L models.}
{The application of SiRi mechanism on other V-L models can be achieved by simply following the principle: keeping the parameters of V-L fusion module continuously training, while reinitializing the other parts. We applied our SiRi to Transformer-based Visual Grounding model TransVG~\cite{TransVG} and RES model LAVT~\cite{yang2022lavt}. Experimental details are presented in
Appendix~\ref{subsec:transvg_lavt}.}
For TransVG~\cite{TransVG}, we report REC and Phrase Grounding results in Table~\ref{tab:TransVG}. We found that SiRi could further improve the performance of TransVG by an average of 2\% at top-1 accuracy on all four REC datasets, and the performance has also been effectively improved on Phrase Grounding dataset Flickr30k dataset. For LAVT~\cite{yang2022lavt}, We report the results of SiRi in RES dataset RefCOCO+ three splits \textit{val}, \textit{testA}, \textit{testB} in Table~\ref{tab:lavt_result}.

\begin{table}[!t]
\centering
\caption{REC and phrase grounding results of TransVG~\cite{TransVG} with SiRi mechanism.}
\scalebox{0.85}{
\begin{tabular}{c|c|ccc|ccc|c|cc|cc}
\toprule
\multirow{3}{*}{Model} & \multirow{3}{*}{Backbone} & \multicolumn{9}{c|}{Referring Expression Comprehension}                                                                                & \multicolumn{2}{c}{PhraseGround}  \\
\cline{3-13}
                       &                           & \multicolumn{3}{c|}{RefCOCO}                        & \multicolumn{3}{c|}{RefCOCO+}                       & RefCOCOg        & \multicolumn{2}{c|}{ReferIt}    & \multicolumn{2}{c}{Flickr30k}        \\
                       &                           & val             & testA           & testB           & val             & testA           & testB           & g-val           & val            & test           & val            & test                \\
\hline\hline
TransVG                & ResNet-50                 & 80.49           & 83.28           & 75.24           & 66.39           & 70.55           & 57.66           & 66.35           & 71.60          & 69.76          & 77.19          & 78.47               \\
+SiRi                  & ResNet-50                 & \textbf{82.97}  & \textbf{84.42}  & \textbf{79.04}  & \textbf{69.30}  & \textbf{73.27}  & \textbf{59.93}  & \textbf{68.54}  & \textbf{74.28} & \textbf{71.36} & \textbf{77.99} & \textbf{79.17}      \\
\bottomrule
\end{tabular}}
\label{tab:TransVG}
\end{table}

\begin{table}[!t]
\centering
\caption{Referring Expression Segmentation results of LAVT~\cite{yang2022lavt} with SiRi.}
\scalebox{0.8}{
\setlength{\tabcolsep}{3.5mm}{
\begin{tabular}{c|c|ccccc|c|c}
\toprule
RefCOCO+               & Model & P@0.5          & P@0.6          & P@0.7          & P@0.8          & P@0.9          & oIoU           & mIoU            \\
\hline\hline
\multirow{2}{*}{val}   & LAVT  & 74.44          & 70.91          & 65.58          & 56.34          & 30.23          & 62.14          & 65.81           \\
                      & +SiRi & \textbf{75.56} & \textbf{72.39} & \textbf{67.88} & \textbf{58.33} & \textbf{30.79} & \textbf{62.86} & \textbf{66.78}  \\
\hline
\multirow{2}{*}{testA} & LAVT  & 80.68          & 77.96          & 72.90          & 62.21          & 32.36          & 68.38          & 70.97           \\
                      & +SiRi & \textbf{82.20} & \textbf{79.18} & \textbf{74.54} & \textbf{63.99} & \textbf{32.62} & \textbf{68.87} & \textbf{71.93}  \\
\hline
\multirow{2}{*}{testB} & LAVT  & 65.66          & 61.85          & 55.94          & 47.56          & 27.24          & \textbf{55.10} & 59.23           \\
                      & +SiRi & \textbf{66.41} & \textbf{62.86} & \textbf{57.37} & \textbf{49.23} & \textbf{27.90} & 55.03          & \textbf{59.70}  \\
\bottomrule
\end{tabular}}}
\label{tab:lavt_result}
\end{table}

\noindent \textbf{Extend to other V-L tasks.} We also test our SiRi in more vision-language tasks, including referring expression segmentation, phrase grounding, and visual question answering.
For these experiments, we took the transformer-based MDETR model (without pre-training) as our baseline.
The specific settings of how to apply SiRi on these tasks are stated as follows.

\noindent \textbf{\textit{-Referring Expression Segmentation (RES).}} RES is to segment the objects according to the given language description. We further perform the segmentation task on the trained visual grounding model. We keep the original MDETR model architecture the same but modify the hyperparameters according to the settings used in training visual grounding in this paper.
We test the SiRi model on three RES datasets, \textit{i.e.}, RefCOCO, RefCOCO+, RefCOCOg.
In Table \ref{tab:res}, we report the RES performance of the SiRi model after \textit{Initial-train}, \textit{3rd-train}, and \textit{5th-train} stages.
It can be seen that SiRi can steadily improve RES models during the retraining process.

\begin{table}
\centering
\caption{Experiment results on RES. We report precision Pr@0.5, 0.7, 0.9 and overall IoU on the \textit{val} set of RefCOCO, RefCOCO+, RefCOCO.}
\label{tab:res}
\scalebox{0.8}{
\begin{tabular}{c|cccc|cccc|cccc}
\toprule
\multirow{2}{*}{Stage} & \multicolumn{4}{c|}{RefCOCO}     & \multicolumn{4}{c|}{RefCOCO+}    & \multicolumn{4}{c}{RefCOCOg}      \\
                       & Pr@0.5 & Pr@0.7 & Pr@0.9 & oIoU  & Pr@0.5 & Pr@0.7 & Pr@0.9 & oIoU  & Pr@0.5 & Pr@0.7 & Pr@0.9 & oIoU   \\
\hline\hline
Initial-train          & 77.76  & 68.89  & 28.58  & 62.12 & 68.36  & 61.11  & 25.89  & 52.48 & 64.34  & 54.84  & 20.42  & 51.39  \\
3rd-retrain            & 82.58  & 74.33  & 32.57  & 68.02 & 75.27  & 67.76  & 28.21  & 60.11 &  72.20      &61.46        &25.12        &58.33        \\
5th-retrain            & \textbf{83.56}  & \textbf{75.37}  & \textbf{32.79}  & \textbf{69.34} & \textbf{76.46}  & \textbf{68.47}  & \textbf{28.26}  & \textbf{61.1}5 & \textbf{73.24} & \textbf{63.25}   &  \textbf{25.08}      & \textbf{59.69}       \\
\bottomrule
\end{tabular}}
\end{table}

\noindent \textbf{\textit{-Phrase Grounding.}}
The task is to locate objects in an image based on the phrases which may be inter-related.
We evaluate the SiRi mechanism on the Flickr30k entities dataset. For the input image, we set the maximum size to 800.  We show the model performance of different SiRi stages in Table \ref{tab:pg_gqa}.
We can see SiRi further improves the initial trained model by 1\%$\sim$2\% on Recall@1, Recall@5, Recall@10 (denoted as R@1, R@5, R@10, respectively).

\noindent \textbf{\textit{-Visual Question Answering.}} Given an image and a question in natural language, this task is to infer the correct answer. We use the scene graph provided in GQA to align question words and the boxes as in MDETR. We verify the validity of SiRi on the visual question answering task in GQA \textit{balanced} split dataset.
The results of SiRi model from different training stages are reported in Table \ref{tab:pg_gqa}. The accuracy is improved from 55.75 to 57.45.

\begin{table}[!t]
\centering
\small
\caption{Experiment results of Phrase Grounding on the validation set of Flickr30k and the VQA performance on the GQA \textit{balance test} set.}
\scalebox{0.85}{
\setlength{\tabcolsep}{8.5mm}{
\begin{tabular}{c|ccc|c}
\toprule
\multirow{2}{*}{Stage} & \multicolumn{3}{c|}{Phrase Grounding@Flickr30k} & GQA  \\
                       & R@1   & R@5   & R@10                            & Accuracy     \\
\hline\hline
Initial-train          & 76.22 & 87.19 & 90.26                           & 55.75    \\
1st-retrain            & 78.41 & 88.42 & 91.31                           & 56.38    \\
2nd-retrain            & \textbf{78.63} & \textbf{88.62} & \textbf{91.62}                           & \textbf{57.25}    \\
\bottomrule
\end{tabular}}}
\label{tab:pg_gqa}
\end{table}

\section{Conclusion}
In this paper, we present a novel training mechanism namely Selective Retraining (SiRi) for visual grounding, where we keep updating the Transformer encoder while re-initialize the other modules to get out of local minimums.
We further propose multi-task SiRi to train a better encoder by incorporating an auxiliary decoder with constant input queries.
Extensive experiments prove our method helps the Transformer encoder better perceive the relationship between the visual and the corresponding expression, outperforming state-of-the-art methods on the three visual grounding datasets.
Interestingly, we find SiRi also performs superior even with very limited training data.
Even with a quarter of training data, we outperform state-of-the-art methods (with full training data) by 1.65\% on the RefCOCOg validation set.
We also extend SiRi to other Transformer-based visual grounding models and other V-L tasks. We hope our work will help motivate more researchers in the V-L research community in the future.

\textbf{Acknowledgements.}
This work was supported in part by the National Key R\&D Program of China (No.2021ZD0112100), the National NSF of China (No.U1936212, No.62120106009), the Fundamental Research Funds for the Central Universities (No.K22RC00010). We thank Princeton Visual AI Lab members (Dora Zhao, Jihoon Chung, and others) for their helpful suggestions.

\newpage
{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


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