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CVPR2019参加速報 本会議 2日目 / CVPR2019 Personal Memo: Day 2
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Atsushi
June 20, 2019
Technology
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220
CVPR2019参加速報 本会議 2日目 / CVPR2019 Personal Memo: Day 2
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 20, 2019
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Transcript
CVPR2019 The Main Conference - the 2nd day -
- ࡢ·Ͱͱಉ༷ɺݸਓͷϝϞΛެ։͍ͯ͠ΔΑ͏ͳܗͷͷͰ͢ɻ͋͘·ͰɺͪΒ͠ͷཪతͳ ѻ͍Ͱ͓ئ͍͠·͢ɻ - ࢲͷཧղͷޡΓଟʑ͋Δͱࢥ͍·͢ͷͰɺ͝༰͍ࣻͩ͘͞ɻ - ϝϞ: visualcommonsense.com (visual commonsense
reasoning) Vision-and-language navigationͱ͍͏λεΫʹ͍ͭͯࢥͬͨ͜ͱɻ - ఆ͍ͯ͠ΔϩϘοτͷࢦࣔࡉ͔͗͢ΔͷͰͳ͍͔ʁ࣮༻Λߟ͑ΔͳΒʮ͓෩ ࿊ʹ͋ΔYYYΛऔ͖ͬͯͯʯ͚ͩͰࡁ·͍ͤͨɻΒͳ͍ॴͰϩϘοτΛOBWJHBUJPO ͢Δ࣮༻ੑͲ͜ʹ͋Δͷ͔ʁ Βͳ͍֗ͳΒHPPHMFNBQͰྑ͍ ͋Δ͍ݴޠͰࢦࣔ͢ΔOBWJHBUFSΒͳ͍ॴͳΒݴޠʹΑΔΓͱΓෆཁʁ ࠷ऴతʹϩϘοτ͕ਓؒΛφϏήʔτ͢Δͷ͔ʁͰɺͲ͏φϏήʔτ͖͔͢Λࣗ ͕ኲኾ͏͜ͱͰֶशɻͦΕͳΒΘ͔Δ͔ɻ
લఏ: ਖ਼ղϥϕϧͷ͚ํҟ ͳΔ͕ಉ͡࡞ۀΛ͍ͯ͠Δө ૾ʹର͢Δσʔληοτ͕͋ Δɻ త: ্ख྆͘ํΛͬͯਫ਼ Λ͍͋͛ͨɻ 2ͭͷσʔληοτΛࠞͥͨ batchΛ࡞Δɻશ෦ʹରͯ͠ಉ
ҰͷBNΛ͔͚Δͱ͕ҧ͍ ্͗ͯ͢ख͍͔͘ͳ͍ɻ·ͬ ͨ͘ҧ͏BNΛ͔͚Δͱσʔλ Λ߹ΘͤͨԸܙ͕ͳ͍ɻ ޓ͍ʹɺҰఆΛࠞͥΔΑ͏ ʹ͢ΔɻԿݸࠞͥΔ͔ learning to learnతʹ(?)ࣗಈਪ ఆ͠ͳ͕ΒΔɻ ࣮ݧ݁ՌΛΈΔͱɺҧ͏࡞ۀ ͷσʔληοτΛࠞͥͯྑ ͘ͳΒͳ͍͕ɺྉཧಉ࢜ͩͱ ྑ͘ͳΔɻ 㯪
ըૉʹର͢ΔఢରతPertubationͰͳ͘ɺಛ ྔʹର͢ΔPertubationΛ࡞͢ΔͱΫϥε/Ϋ ϥεؒࢄൺ͕ྑ͘ͳΔɻ ଟɺͭԼͷൃදͱؔ࿈͍ͯͯ͠ɺಛۭؒ ͰͷQFSUVCBUJPOؤ݈ੑΛ͋͛Δྑ͍%BUB "VHVNFOUBUJPOʹͳ͍ͬͯΔͨΊͩͱࢥ͏ɻ ਂ͍ಛྔʹରͯ͜͠ΕΛΔͷͱɺදͰಛ ʹऩ·ΔൣғͰఢରతQFSUVCBUJPOΛੜ ͢Δͷ΄΅ಉͣ͡ɻ QFSUVCBUJPOઁಈ
ࣅͨΑ͏ͳൃද͕ଟ͗ͯ͢ৄࡉΛΕͯ͠ ·ͬͨ... Cycle GANΛ͍ͬͯΔͷ֬ ͔ɻ
2্ͭͷൃදʹࣅ͍ͯΔɻ ఢରతpertubationΛɺ࣮ࡍͷσʔλ ͕࡞ΔmanifoldͷதͰ࡞Δͱؤ ݈ੑ͕͕͋Δɺͱ͍͏ใࠂɻ ͜Εɺ͏Data Augumentationͷε λϯμʔυͷҰͭͰྑ͍͔ɻ
υϝΠϯ͚ͩҟͳͬͯଞ͕Ұக͢Δ ϖΞ͕ඞཁͦ͏?Few-shotͱ͍ ͑ɺͦΜͳσʔλΛϦΞϧͰͱΔͷ ͍͠ͷͰ...ͱࢥͬͨɻ ͪΐͬͱࢥ͍ͬͯͨͷͱҧͬͨͷ Ͱɺࣸਅͱͬͨޙɺଟ͘ಡΈࠐΜ Ͱ͍ͳ͍ɻ
ݕग़͞ΕͨBBʹɺੲͳ͕Βͷ SFHJPO proposalΛͨ͘͞Μੜ ɻ Ͳ͏ͯ͠sparse annotationͰOK ʹͳΔͷ͔ɺલఏͳͲؚΊͯ Α͘Θ͔Βͳ͔ͬͨɻ
private branchͱcommon branch ʹ͚Δܗͷtwo-streamܕͷ disentangling. ೋͭͷbranchͷಛ ͕ߦ͢ΔΑ͏ͳlossΛೖΕ Δɻͬͯɺ͜ΕɺԼهͷจʹࠅ ࣅɻ https://arxiv.org/pdf/
1804.09347.pdf
DomainຖʹBatch NormΛ͔͚Δ ͱྑ͍Αɺͱ͍͏ൃදɻ ͜ͷPDFͷ࠷ॳͰհͨ͠ख๏ ͷԼҐޓ???
͜ΕࣸਅΛऔͬͨ͋ͱ Ͱɺࢥͬͯͨͷͱҧͬͨͷ ͰૣΊʹ࣍ʹҠಈ Γ ܦͭͱৄࡉΛΘ͢Ε͕ ͪ
Video Clipʹରͯ͠ɺ͋ΔtaskΛ͢Δͱ͖ʹͲͷମΛ͏͔Λݕग़ɻ MS COCOͷσʔλʹରͯ͠ɺՃͰΞϊςʔγϣϯΛ͍ͯ͠ΔΒ͍͠ɻ ҙ֎ʹɺ2ਓͷΞϊςʔλؒͷਖ਼ղͷҰக90%Λ͑Δͱͷ͜ͱɻ serve wineʹରͯ͠glassͷྖҬΛબ͍ͯ͠ΔͷͰɺಓ۩Λࢦఆ͢ΔΑ ͏ʹ͍ͯ͠ΔͬΆ͍ʁ(wine bottleͰͳ͍...ͳͥ?) Ͱ
Video SummeryΛ࡞ΔࡍͷධՁࢦ ඪ͕ɺϥϯμϜੜʹରͯ͠ߴ ͘ͳͬͯ͠·ͬͨΓɺ৭ʑ͕ ͋ΔͷͰͳ͍͔ɺͱ͍͏ൃදɻ
summeryʹͳ͍ͬͯΔಈըͷू߹Λ realͱͯ͠ɺGANϕʔεͷֶशͰ summeryͰͳ͍ಈըͷมΛ͢Δ ωοτϫʔΫΛֶशɻ ैདྷͱҧͬͯɺݩಈըͱsummery͕ pairʹͳ͍ͬͯͳͯ͘ྑ͍ɻ
ਓ࢟ͷεέϧτϯϞσϧΛdirected graphͱͯ͠Graph NNͰಈ࡞ࣝผɻ ͜Ε͚ͩͰ৽نੑ͕͋Δͷ͔एׯٙͩ ͕...
FairnessΛ୲อ͢ΔͷʹɺMaximal Mean DiscrepancyͰͳ͘ɺSpurious lossͱNon-Spurious lossͱ͍͏ͷΛ ఏҊɻಛʹϝδϟʔͳैདྷख๏ͱͷൺ ֱ͕ͳ͍Α͏ͳ...
ڈͷCVPRͰRLΛͬͯMask R-CNNΛend- to-endʹ͢Δݚڀ͕͕͋ͬͨɺ͜ΕɺෳͷBB ʹରͯ͠Semantic SegmentationΛ͢Δॱ൪ΛRL ͰܾΊΔ͜ͱͰਫ਼্Λࢦͨ͠ͷɻ
Instance SegmentationͱSemantic SegmentationͷؒͷΑ͏ͳΛఏҊɻ ͭ·Γɺಉ͡Semantic LabelͷྖҬͰɺ Πϯελϯε͕ྡΓ߹͍ͬͯΔ߹ʹɺͦͷڥ քݕग़͢ΔͬΆ͍ɻ
ੲͳ͕ΒͷώϡʔϦεςΟοΫͳ Region Proposal͔ΒUnsupervisedʹ ΑΓྑ͍Region ProposalΛੜ͢Δ ωοτϫʔΫΛֶशɻ
Ωϟϓγϣϯͷ֤entityʹର͠ ͯɺରԠ͢ΔྖҬͷBBΛΞϊς ʔγϣϯͨ͠৽͍͠σʔληο τΛެ։ɻ SupervisedʹτϨʔχϯάͨ͠ ϞσϧͰɺGrounding͕Ͱ͖Δ ͜ͱ֬ೝɻ
Entityͷ֓೦ϨϕϧʹԠͨ͡ hierarchicalͳWord embeddingͰɺ ը૾ͱςΩετͷΑΓΑ͍ڞ௨જࡏ ۭؒΛֶशɻ
Vision-Language NavigationͰ Imitation Learning. Ͳ͏ͯ͠Self- supervisedͳImitation Learning͕ Γཱͭͷ͔ྑ͘Θ͔Βͳ͔͕ͬͨɺ ਓ͕ଟ࣭ͯ͘Ͱ͖ͣɻ
τϤλͷंɻςεϥͷํ͕֨ྑ͔ͬ ͨɻࣸਅͳ͍͚Ͳɻ
ࡢͷࡈ౻͞Μͷख๏ΛɺMMDͰ ͳ͘ɺMaximum Classifier DiscrepancyͰম͖ͨ͠Α͏ͳख ๏ʹݟ͑ΔɻIncremental?
SpectralNetωλ͕ٸʹ૿͑ͨҹɻ ͜ΕɺΫϥελϦϯά͢Δͱ͖ʹɺಉ࣌ʹ GANͰfakeαϯϓϧੜͤ͞Δ͜ͱͰɺ ΫϥελϦϯάͰself-paced learningΛ࣮ݱ͢ Δํ๏ͱͷ͜ͱɻ ͪΐͬͱself-paced learningʹ͍ͭͯͷษڧ͕ Γͳ͍͔Β͔ɺશʹཧղͰ͖ͣɻ
Cycle GANͩͱɺgradientΛ อ࣋͢Δม͕Ұഋɻ Cycle consistencyͰͳ͘ɺ AEΛ2ͭʹͯ͠ɺA-Bมͷ ෦ͱAEx2ΛಠཱʹֶशͰ͖ ΔΑ͏ʹ͢Δ͜ͱͰϝϞϦޮ Λ͋͛Α͏ɺͱ͍͏ͩͱ ཧղɻ
͜Εɺ͔ͳΓ໘ന͔ͬͨ Overfitting͕࢝·Δͱω οτϫʔΫͷϊʔυؒͷ ڞىؔʹ໌֬ͳมԽ͕ ݱΕ͍ͯΔͷͰʁͱ͍ ͏ͷΛɺάϥϑͷύϥϝ λͰଌͬͨͷɻ ͜͜Ͱ Overfitting͕࢝ ·͍ͬͯΔΒ
͍͠ɻ t.TT?
Adversarial attack ʹΑͬͯޡࣝผ͕ ى͖Δͷ͕ɺ΄ͱ ΜͲൃՐ͠ͳ͍χ ϡʔϩϯ͕ൃՐ͠ ͯ͠·͏͜ͱͰ͋ Δͱ͍͏Ծઆͷ ͱɺΧςΰϦ͝ͱ ʹɺྑ͘ΘΕΔ
NN্ͷܦ࿏Ҏ֎ Λແࢹͯࣝ͠ผ͢ ΔΑ͏ʹ͢Δ͜ͱ Ͱɺdefense͢Δ (ޡࣝผΛ͙)ɻ
Interclass domain discrepancyΛ૿͠ ͯɺ Intraclass domain discrepancyΛԼ͛ Δɻ Target domainϥϕϧ͕ͳ͍ͷͰɺ
source domainͷ֤Ϋϥεͷಛۭؒ ʹ͓͚Δத৺ΛؚΉɺेʹີͷߴ ͍ΫϥελΛclassͱΈͳ͢ɻ ͪΐͬͱΞυϗοΫͳײ͕͢͡Δɻ
BB͍͋ͬͯͯΧςΰϦΛؒҧ͍͑ͯΔΑ͏ͳ݁ՌΛ্ख͘ϦαΠΫϧ͢Δɻಉ ͡ը૾Ͱݕग़͞ΕͨଞͷϥϕϧͱͷڞىසͳͲΛ͏ͬΆ͍ɻ ·ͨɺBBͷݕग़ʹՃ͑ͯɺ2ͭͷBBͷۣۙ͞ܗΛಉҰϥϕϧͱΈͳͨ͠ (ڧҾͳ)SegmentationλεΫΛՃ͑ͨmulti-task learningʹ͢Δɻ ͜ΕେΞυϗοΫ...
௨ৗͷUDAʹՃ͑ͯ ୯ʹSource Domainͷࣝผ͚ͩΛߦ ͏ಛ+Densex1ΛՃ͑Δ͜ͱ Ͱɺͪΐͬͱ͚ͩਫ਼্͕͢Δɻ SOTAΑΓେ͍Ͷɺͱฉ͍ͨ ΒɺͰͲͷख๏ʹ͢͜ͱ͕Ͱ ͖ΔΑɺͱͷ͜ͱɻ
֤ը૾ʹର͢ΔSegmentation݁ ՌΛ࣌ܥྻํʹΒ͔ʹͳΔ Α͏ɺVideoΛ༻͍ͯՃֶश͢ Δख๏ɻ
SpectralNet͕ൺֱख๏ʹͳ͔ͬͨͷͩ ͕ɺ͍ͬͯΔ͜ͱͷ SpectralNetͰͳ͍ͩΖ͏͔...???
DESiREͱ͔ͷྲྀΕͳΜͩͱࢥ͏ͷ͚ͩΕͲɺͲ͕͜Ұ൪ͷ৽نੑͳͷ͔ ಡΈऔΕΔ΄Ͳɺ͜ͷʹৄ͘͠ͳ͔ͬͨɻ
GANͱผͷํ๏Ͱإͷ߹Λ͠Α͏ͱ͢Δख๏ɻ ैདྷख๏Ͱ͋ΔIMLE͕Θ͔Βͳ͍ͷͰɺͪΐͬͱԿΛ͍ͯ͠Δ͔Θ͔Βͳ͔ͬͨɻ
AdaINΛఏҊɻຖʹཚΛม͑ͨͱ͖ʹมԽ͢Δͷ͕ҧ͏ͷͰɺσϞ͕Impressive. https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans- generating-and-tuning-realistic-6cb2be0f431
None