Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
CVPR2019参加速報 本会議 3日目 / CVPR2019 Personal Memo: ...
Search
Sponsored
·
Ship Features Fearlessly
Turn features on and off without deploys. Used by thousands of Ruby developers.
→
Atsushi
June 21, 2019
Technology
0
270
CVPR2019参加速報 本会議 3日目 / CVPR2019 Personal Memo: Day 3
チラシの裏チラシの裏チラシの裏チラシの裏チラシの裏チラシの裏
Atsushi
June 21, 2019
Tweet
Share
More Decks by Atsushi
See All by Atsushi
CVPR2019参加速報 本会議 2日目 / CVPR2019 Personal Memo: Day 2
atsushihashimoto
0
230
CVPR2019 参加速報 本会議1日目 / CVPR2019 Personal Memo: Day 1
atsushihashimoto
0
360
Other Decks in Technology
See All in Technology
20260204_Midosuji_Tech
takuyay0ne
1
160
プロダクト成長を支える開発基盤とスケールに伴う課題
yuu26
4
1.4k
【Oracle Cloud ウェビナー】[Oracle AI Database + AWS] Oracle Database@AWSで広がるクラウドの新たな選択肢とAI時代のデータ戦略
oracle4engineer
PRO
2
180
顧客との商談議事録をみんなで読んで顧客解像度を上げよう
shibayu36
0
290
Codex 5.3 と Opus 4.6 にコーポレートサイトを作らせてみた / Codex 5.3 vs Opus 4.6
ama_ch
0
190
Claude_CodeでSEOを最適化する_AI_Ops_Community_Vol.2__マーケティングx_AIはここまで進化した.pdf
riku_423
2
610
学生・新卒・ジュニアから目指すSRE
hiroyaonoe
2
740
SREが向き合う大規模リアーキテクチャ 〜信頼性とアジリティの両立〜
zepprix
0
470
コンテナセキュリティの最新事情 ~ 2026年版 ~
kyohmizu
6
1.2k
配列に見る bash と zsh の違い
kazzpapa3
3
170
AIエージェントを開発しよう!-AgentCore活用の勘所-
yukiogawa
0
180
Oracle Cloud Observability and Management Platform - OCI 運用監視サービス概要 -
oracle4engineer
PRO
2
14k
Featured
See All Featured
Un-Boring Meetings
codingconduct
0
200
The Impact of AI in SEO - AI Overviews June 2024 Edition
aleyda
5
740
Winning Ecommerce Organic Search in an AI Era - #searchnstuff2025
aleyda
1
1.9k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
21
1.4k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
508
140k
The Organizational Zoo: Understanding Human Behavior Agility Through Metaphoric Constructive Conversations (based on the works of Arthur Shelley, Ph.D)
kimpetersen
PRO
0
240
AI Search: Where Are We & What Can We Do About It?
aleyda
0
7k
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
350
Why Our Code Smells
bkeepers
PRO
340
58k
Navigating the Design Leadership Dip - Product Design Week Design Leaders+ Conference 2024
apolaine
0
190
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
470
Art, The Web, and Tiny UX
lynnandtonic
304
21k
Transcript
CVPR2019 Main Conference the 3rd day
Deep LearningΛ͍ͬͯΔ ͷ͕ͩɺLosslessͰը૾Λѹ ॖ͢Δख๏ɻ ֤ղ૾ͰEntropyූ߸ԽΛ ͬͨEncoding ˠղ૾ͷ͍ը૾ͱͯ͠ ͕ࠩͰΔ(͜͜ͷৄࡉ͕Θ͔ Βͳ͔ͬͨ) ˠ(܁Γฦ͠)
ˠখ͞ͳը૾͕ࠩ0ʹͳ ΔΑ͏ʹEntropyූ߸Խ σίʔυٯॱͰɻ Poster൪߸1൪ʹ ϙελʔηογϣϯ ։࢝ޙʹ͍͘ͱ શ͚ۙͮ͘ͳ͍ͷͰ ҙɻ •
1࣍ݩʹslice͢ΔWasserstein Distance ˠn࣍ݩʹslice͢ΔWD(͜͜·Ͱैདྷख๏) n͕Exponentialʹେ͖͘ͳΔ͕͋ͬ ͨɻ ͦͷͨΊɺn࣍ݩͷmaxΛڑͱ͢ΔWD ΛఏҊɻڑͷެཧΛຬͨ͢ɻ ·ͨɺnఆͰྑ͍ɻ
Ϩγϐʹ͓͚Δ ը૾⁶खॱจॻͷ ຒΊࠐΈɻ ςΩετ͔Βը૾Λੜ ͠ɺGANͰຊͬΆ͘͢Δɻ ڞ༗જࡏ্ۭؒͷಛྔ͚ͩ Ͱͳ͘ɺը૾ͷྨࣅߟྀ ʹ͍Εͯݕࡧ͢Δ͜ͱͰੑೳ ্͕ͱͷ͜ͱɻ (ٯʹݴ͑ɺಛྔͷൺֱͩ
͚Ͱ͋·ΓޮՌ͕ͳ͍ʁ)
ࡢͷStudent Best PaperͰ͋Δ Total Captureͷଓฤɻ ࡢإମɺखͷ࢟ͷਪఆ ผʑʹͬͯɺଓ෦Ͱແཧ ΓҐஔ߹Θ͍͚ͤͯͨ͠Ͳɺ͜Ε Λ͏গ͠౷߹తͳਪఆʹͨ͠ ͷɻओʹɺखإ౷߹ͨ͠Part
Orientation FieldsͱɺMesh TrackingʹΑΔମද໘ͷಈ͖ͷਪ ఆ͔ΒͳΔɻ ֶश༻σʔλ૿༷͑ͨɻ Tired
ը૾͔Β࢟Λਪఆ͢Δω οτϫʔΫΛ͍ͭͭɺը ૾Λճసͤͯ͞ɺ͝ʹΐ͝ ʹΐͱ͢Δ͜ͱͰ࢟Ҏ֎ ͷಛΛநग़͢Δωοτϫ ʔΫΛఏҊɻ
Flickerͷhash tagΛͬͯɺͱʹ͔ ͨ͘͘͞ΜσʔλΛͬͯpre- training͢Δ͜ͱΛతͱͨ͠ख ๏ɻhash tagͷ͏ͪɺಈ࡞ͱؔ ͳ͍ͷଟ͍ͷͰɺطଘͷσʔ ληοτͷΞϊςʔγϣϯͱྨࣅ ͨ͠λάͷΈΛऑڭࢣσʔλͱ͠ ͯ࠾༻ɻҎ্ͷॲཧΛશͯࣗಈ
Ͱɺ1ϲֶ݄͔͚ۙͯ͘शͨ͠ͱͷ ͜ͱɻ 0 -_- ͍ͭͯ ͱͯ͠
લఏ: ಉ͡खॱͷ࡞ۀΛͨ͠ಈը͕ෳ ͋Δɻ త: ࡞ۀͷதͷಈ࡞ΧςΰϦΛɺͦͷ ಛྔͱͱʹࣗಈͰಛఆɻ ํ๏: ࡞ۀͷ։͔࢝Βऴྃ·Ͱͷ࣌ࠁtΛ 0ʙ1Ͱද͢ɻ ֤࣌ࠁͷը૾ྻΛೖྗͱͯ͠ɺtΛਪఆ
͢Δself-supervised learningΛߦ͏ɻ (ಈ࡞ͷॱ൪ʹ͕͋ΔͳΒಛྔͱt ͕݁ͼͭͣ͘ɺͱ͍͏Ծఆ) ͜͏ͯ͠ಘΒΕͨಛྔΛΫϥελϦϯ ά͢Δͱɺಈ࡞ΫϥεʹͳΓಘΔɻ
Grassman Manifolds্ͷʹରͯ͠ɺࣜ(3)ͷ Α͏ʹೋͭͷΧʔωϧΛ༻͍ͨڑ(?)Λ spectral clusteringʹΑΓ࠷খԽɻ ΧʔωϧΛೋͭ༻͍Δ͜ͱͷϝϦοτྑ͘ Θ͔Βͳ͔ͬͨɻGrassman Manifolds্Ͱ Spectral Clusteringɺͱ͍͏ͷੲMulti-layer
Spectral Clusteringͱ͍͏จͰಡΜͩΑ͏ʹ ࢥ͏͠ɺͦ͜ʹৄ͘͠ॻ͍ͯ͋ͬͨͣͳͷ ͰɺͪΌΜͱཧղ͠Α͏ͱࢥͬͨΒɺ͔ͦ͜ ΒಡΈͯ͠ྑ͍͔ɻ
͜ΕɺD2AE(CVPR2018)Λ VAEͬͯ៉ྷʹ࣮ͨ͠ ͚ͩͰ???
Monocular DepthͰɺ࣌ܥྻ؍ଌΛੵ ͢Δ͜ͱͰਫ਼Λ͋͛Δख๏ɻ
Event Camera (ޫྔͷ૿ݮͷΈΛଊ ͑ΔΧϝϥ)ʹ͓͚Δػցֶश༻ͷଛ ࣦؔͷྑ͠ѱ͠Λௐͨαʔϕ Πɻ ͜͜ʹͨ͘͞Μྻڍ͞Ε͍ͯΔɻ Β Ұ
Monocular DepthΛ͢Δ࣌ ʹྨࣅ͢ΔλεΫ(optical flowͱ͔৭ʑ)Λͬͨmulti- taskʹ͠Α͏ɺͱ͍͏ͱ͜ Ζ·ͰڈʹͲ͔͜Ͱݟ ͨؾ͕͢Δɻ ͪΐͬͱͲ͜ʹ৽نੑ͕͋ Δͷ͔·Ͱ͑ΒΕ͍ͯ ͳ͍ɻ
Unsupervised Domain AdaptationΛ͢Δͱ͖ʹɺGANΛͬͯυϝΠϯΛ૿ͯ͠ؤ݈ੑ Λ ͋͛Δख๏ɻී௨ͷreconstruction loss + adv. loss, color
consistency loss + adv. loss, fullͷ 3छྨͰυϝΠϯΛ3ͭ૿͢(Domain Diversification... ͍ͩͿώϡʔϦεςΟοΫ͕ͩ). ͦͷ্ͰɺMulti-source domain adaptationΛߦ͏ɻମݕग़ͰධՁɻ
ࣸਅʹରͯ͠ɺࣗવݴޠͰ༩͑ͨΫΤ ϦʹΑͬͯɺͲͷ෦͕ݴٴ͞Ε͔ͨ Λattentionͱͯ͠औΓग़͢ख๏ɻ
ࣝผ݁Ռʹରͯ͠ɺͳͥͦ͏அͨ͠ͷ͔ ͑ΒΕΔΑ͏ʹͨ͠Ϟσϧɻ આ໌ ςΩετ+BBͰ༩͑ΒΕΔɻ આ໌ͷੜ෦ࣄલʹڭࢣ͋ΓͰֶ शɻ ʔ
ಉ͘͡આ໌Λੜ͢Δݚ ڀɻ # ྆ํͱ౦େݪాݚ ࣸਅͰͪΐ͏ͲӅΕͯ͠ ·͍ͬͯΔ͕ɺઆ໌Λ Because ࣝผ݁Ռͷௗͷಛ ͷઆ໌ as
ը૾1, not as ը ૾2ɺΈ͍ͨͳܗͰ 1. ಉ͡ಛΛͭଞͷௗ 2. ͦͷಛΛ࣋ͨͳ͍͕ࣅ ͨௗ Λදࣔ͢Δɻ
Hand-crafted ͳWarpingؔΛೖ ΕΔ͜ͱͰdisentanglingΛ͢Δख ๏ɻwarpingؔͰRealͳը૾ ΛੜͰ͖ͳ͍ͷͰɺ݁ՌͷΫΦ ϦςΟͪΐͬͱѱ͍Α͏ͳؾ ͢Δɻ
Video frameͷॱংΛsort͢Δself-supervised learningख๏CVPR2017Ͱൃද͞Ε͍ͯΔ ͕ɺframeͰͳ͘ɺvideo clipͷsortΛ͢Δख ๏ɻ 2017ͷࠒΑΓGPUͷmemoryαΠζ͕૿͑ ͯྑ͔ͬͨɻ
UDAΛ͢Δͱ͖ͷDiscriminator ͱͯ͠1࣍ݩͷSliced Wasserstein DistanceΛ༻͍ Δɻ ΧςΰϦؒʹҙͷॱংؔΛ ࡞ΓɺΧςΰϦؒͷWDΛ(ॱং ʹԠͯ͡)େ͖͘͠ͳ͕Βɺυ ϝΠϯؒͷڑΛখ͘͢͞Δ͜ ͱͰΑΓΑ͍UDAΛ࣮ݱɻ
ޙड़͢Δ͕ɺ͜Ε͔ͳΓ͍͍ ײ͕ͩ͡SOTAͰͳ͍ɻ
ಈըͷதʹɺ΄͔ͷಈը͔ΒಘΒΕ ͨಈମͷը૾ྻΛࣗવʹຒΊࠐΉ ͜ͱͰɺମݕग़ͷ܇࿅σʔλΛਫ ૿͠ɻ
࠷ऴϑϨʔϜ͔Βͷܦա࣌ࠁΛ conditionͱͯ͠ར༻͢Δ͜ͱͰɺ ҙͷ࣌ࠁͷanticipationΛ͢Δख๏ɻ Epic KitchenͰ60ඵઌ·Ͱ༧ଌɻ ಉ͡࡞ۀ͕ଓ͔͘Ͳ͏͔ͯΒΕ ͍ͯΔ͕ɺ֬৴ͷΑ͏ͳͷಉ ࣌ʹग़ྗ͍ͨ͠ͱײͨ͡ɻ ͳ͓ɺ͋·Γԕ͘ͷະདྷ·Ͱཉுͬ ֶͯशͤ͞Δͱਫ਼͕Լ͕Δɺͱ͍
͏ͷͬͯΈͨΒ͍͠ɻ ࠷ॳɺEpic KitchenͷAction Anticipation͜͏͍͏λεΫͩͱࢥ ͬͯͨɻ1ඵઌΛ༧ଌ͢ΔΜ͡Όͳ͘ ͬͯ...ɻ
إͷdepthը૾Domain InvariantͰ͋Δɺͱ ͍͏ԾઆͷԼɺdepthΛ༧ଌ͢Δmain branch ͱɺdomainຖͷdomain specific branchΛֶ शɻ֤domain specific branchͷಛ͕main
branchͱ۠ผ͔ͭͳ͘ͳΔΑ͏ʹ͢Δ͜ͱͰ generalizationΛߦ͏ɻ depthdomainඇґଘɺͱ͍͏ࣄલ͕ࣝॏ ཁʹݟ͑Δɻٯʹݴ͑ɺdepthηϯαʔͷछ ྨإͷ֯ɺڑͳͲ͕ҧͬͨΒɺ͜ͷख ๏͏·͍͔͘ͳ͍ͣɻ
खͷಛΛ͍ͯ͠Δͣͩ ͕ɺஶऀ͍ͳ͍͠ Inference modelsͷઆ໌ෆ໌ͳ ͷͰɺৄࡉΘ͔Βͣɻ
ೕ༮ࣇͷֶशաఔΛ฿͢ Δ͜ͱͰɺIncrementalͳ ମࣝผΛ࣮ݱ͠Α͏ɺͱ͍ ͏νϟϨϯδɻ σʔληοτɺΧϝϥ͕ ମಈ༳ͷΑ͏ͳಈ͖Λ͠ ͓ͯΓɺͦͷதͰମ ͕(࣋͞Εͯ৭ʑͳ֯ ʹ͞Ε͍ͯΔ͔ͷΑ͏ʹ)͘ Δ͘Δճ͍ͬͯΔɻ
ϥϯμϜʹ৭ʑͳମ͕ॱ ൪ʹ࣋͞ΕΔɻ ;ͭ͏ʹΔͱ Catastrophic Forgetting͕ ى͖ΔͷͰͦΕΛͲ͏ճආ ͠·͠ΐ͏ʁͱ͍͏ఏ ى(ͱ؆୯ͳbase lineΆ͍
खͰ࣋ͨ͠෦ͷԹ͕͔͋ͨͨ ͘ͳΔ͜ͱΛར༻ͯ͠ ͞·͟·ͳମ(ͷ3DϓϦϯλͰҹ ͨ͠ϨϓϦΧ)ΛඃݧऀʹѲͬͯ ΒͬͯɺͲ͜Λ৮͍͔ͬͯͨΛճస ςʔϒϧʹͤͯ360ࡱӨͨ͠σ ʔληοτɻ ϩϘοτͷ࣋Λҙ͍ࣝͯ͠ΔΒ͠ ͍͕ɺखͷܗ͕ਓͱϩϘοτͰҧ͏ ͔ΒɺͲ͏͑ΔΜͩΖ͏͔ɺͱͪ
ΐͬͱࢥ͏͕ɺগͳ͘ͱਓ͕৮ͬ ͯྑ͍ͱஅͨ͠ॴͳͲΘ͔Δ ͔ͳɻ
1ϑϨʔϜ͚ͩਖ਼ղͷsegmentation͕͍͍ͯͯɺͦ ΕΛ͢ΔΑ͏ͳઃఆʹ͓͍ͯɺͦͷ1ϑϨʔ ϜΛͲ͜ʹ͢ΔͱҰ൪ਫ਼͕ߴ͘ͳΔ͔Λఆ͢Δ ख๏ͷఏҊɻجຊతʹը૾2ຕΛ͍ΕͯɺͲͪΒ͕ ྑ͍͔ఆ͢ΔωοτϫʔΫΛ͍ɺόϒϧιʔτ Λͤ͞Δɻ CVPRҰٳ͞Μ͔ͳʁͱࢥͬͨݩڟͷݚڀɻ
UDAͷSOTAɻଞͷख๏͕Adv. Training DiscrepancyΛ࠷খԽ͢Δͷʹର͠ ͯɺਅ໘ʹυϝΠϯຖͷฏۉͱࢄ Λܭࢉͯ͠ Batch Normalizationͷ࣌ʹฏۉ0,ࢄ ͕EͱͳΔΑ͏ʹwhitening͢Δɺͭ· Γਅ໘ͳύϥϝλਪఆʹΑΔख๏ɻ ͜ΕʹՃ͑ͯMin-Entropy
Consensus Lossͱ͍͏͍ͯ͠Δ͕ɺͦΕ͕ ͳͯ͘SOTAɻͳ͓ɺTarget Domain ୯ମͷࣝผ݁ՌΑΓਫ਼͕ߴ͍ɻ
͜ΕɺҰൃ͚ܳͩͲ໘ന͔ͬͨɻ semantic segmentationʹՃ͑ͯɺମத৺ ͕ͦͷըૉ͔ΒΈͯͲͪΒ(&ڑ)ʹ͋Δ͔ɺ ͱ͍͏ํͷใֶशɻ͜ΕʹΑΓɺಉҰ ମͷsegmentͰ͋ͬͯinstanceͷڥքΛ ܭࢉՄೳʹ͍ͯ͠Δɻ ํͷใΛOptical FlowΈ͍ͨʹՄࢹԽ͠ ͨͷ
ʔ
Motion SegmentationͰRegion ProposalΛunsupervisedʹ܇࿅͢ Δख๏ɻ Medical Imagingͱ͔Ͱʹཱͪ ͦ͏ɻ
ӴࣸਅͳͲͷൺֱ࣌ʹɺΑ͋͘ ΔมԽ(রͷࡱӨ݅มԽ)ͱ ϨΞͳมԽ(ݐ͕૿͑ͨɺͳͲ) Λ۠ผ͢Δख๏ɻ 2ຕͷը૾ʹڞ௨͢ΔbranchΛ ͬͨreconstruction݁Ռͱɺೖྗ ը૾ͷޡࠩʹج͍ͮͯϨΞ͔Ͳ͏ ͔Λఆ(ݫີʹBackprop.ͨ͠ ͱ͖ͷgradientͷେ͖͞ɺͱ͍ͬ ͍͕ͯͨɺࠓʹͳͬͯɺޡࠩͰ
ఆ͢ΔͷͱԿ͕ҧ͏ͷ͔Θ͔Βͳ ͘ͳ͍ͬͯΔͳ͏)
ըૉຖʹԻݯΛ͢ Δख๏ͳͲͰɺैདྷ ख๏ͰɺԻݯ͕ө૾ ͷϑϨʔϜʹ͋Δ͜ ͱ͕Ծఆ͞Ε͍ͯͨɻ ͜ͷԾఆΛͣͨ͢Ί ʹɺڞ༗જࡏಛΛֶ शͨ͋͠ͱͰɺಈըຖ ʹΫϥελϦϯάʹΑ ͬͯରԠؔΛٻΊ
ΔɻԻ͔Βಘͨಛͩ ͚͔ΒͳΔΫϥελ ɺը૾ʹ͍ࣸͬͯ ͳ͍ԻݯͱఆͰ͖ Δɻ
Deep NNʹAdaBoostΛద༻ ͢Δͱɺաֶशͯ݁͠Ռ͕ѱ ͘ͳΔɻ͜ͷݪҼΛɺαϯϓ ϧຖͷWeightίϯτϩʔϧ ʹ͋Δʢͭ·Γɺޡࣝผ͢ Δαϯϓϧ͕গͳ͗͢Δͱɺ ޙଓͷClassifier͕ͦΕΒʹ overfit͢Δʣͱߟ͑ɺ category-wiseͳॏΈͷߋ৽
ʹΑΔAdaBoost-likeͳΞϯ αϯϒϧख๏ΛఏҊɻ AdaBoostʹৄ͔ͬͨ͠Βൃ දऀʹײಈ͞Εͨͷ͕ͩɺ ͜Ε(δΣωϨʔγϣϯ)Ϊ ϟοϓ๖͑ͷҰछͩΖ͏͔ʁ
ֶशͷޮԽͷͨΊʹConvolutionͷνϟϯ ωϧΛάϧʔϓʹΘ͚Δʢάϧʔϓؒͷࢬ͕ ফ͑ΔͷͰύϥϝλ͕ݮΔʣɺͱ͍͏ख๏ ʹ͓͍ͯɺGroupֶ͚शͰಘΑ͏ɺͱ͍ ͏ख๏ɻ
ֶशʹ༻͍Δ͖Ͱͳ͍όΠΞεΛֶशͨ͠ ߹ʹϖφϧςΟΛ༩͑Δख๏ɻ ೦͗͢ΔͷόΠΞε͕طͰ͋Δͱ͍͏ ԾఆɻόΠΞεΛ͍ͬͯ͠ΕDAͰ͖Δ͠ɺ DAͰ͖ͨΒόΠΞεΘ͔ΔͷͰɺܲͱཛͷ ؔʹ͋ΔͣͳͷʹɺҰํΛطͱͨ͠Β ͩΊͰ͠ΐ...ࠪಡऀࣄ͠ɺ͛;Μɻ