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computer-vision-survey
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KARAKURI Inc.
May 07, 2021
Research
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computer-vision-survey
Computer Visionの近年の動向のサーベイ
KARAKURI Inc.
May 07, 2021
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Transcript
Computer visionͷۙͷಈͷαʔϕΠ ߴࢤ 1
αʔϕΠͷత 2 Computer vision (CV) ݚڀͷۙͷಈΛΓ͍ͨʂ • ֶशख๏ΛΓ͍ͨ • ωοτϫʔΫͷมભΛΓ͍ͨ
ˠ χϡʔϥϧҎ߱ͷ$7ͷมભ͜Ε·ͰͷಈΛ͘ઙ͘հ
ࠓճ͞ͳ͍͜ͱ 3 • ը૾/ಈըੜҰൠ • ఢରతֶश • ڭࢣ͋Γֶश • ࣗݾڭࢣ͋Γֶश
• ݹయతͳίϯϐϡʔλʔϏδϣϯ ͳͲͳͲɽɽ
ࠓͷྲྀΕ 4 ̍ɽλεΫඇಛԽϞσϧʢը૾ೝࣝͷϞσϧʣͷಈ ̎ɽ֤λεΫʹಛԽͨ͠Ϟσϧͷಈ ̏ɽ·ͱΊ
ͦͷલʹ 5 ɾਆࢿྉ܈ ɾͪ͜ΒͷࢿྉΛେ͍ʹࢀߟʹ͠·ͨ͠ http://xpaperchallenge.org/cv/ https://github.com/hirokatsukataoka16/cvpaper.challenge-summary
̍ɽλεΫඇಛԽϞσϧͷಈ 6
ΞʔΩςΫνϟɾֶश๏ʢը૾ೝࣝʣ 7
࣌ܥྻ 8
AlexNet [Krizhevsky+ NeurIPS 2012] 9 • ը૾ೝࣝίϯϖͰ͋ΔILSVRC2012Ͱѹউ • ਂΈࠐΈχϡʔϥϧωοτϫʔΫ(CNN)ͷ࣌ͷນ։͚
࣌ܥྻ 10
ResNet [He+ CVPR 2016] 11 • ILSVRC2015༏উϞσϧ • Skip connectionͷಋೖͰ152ͷਂCNNͷֶश͕Մೳʹ
• Ҏ߱ͷը૾ೝࣝͷϞσϧجຊతʹResNetͷվྑ
࣌ܥྻ 12
ResNext [Xie+ CVPR 2017] 13 • ೖྗΛذͤͯ͞ෳͷωοτϫʔΫͰॲཧ͠ɼͦͷ݁ՌΛ͠߹ΘͤΔ
WideResNet [Zagoruyko+ 2017] 14 • ਂ͞Λઙͯ͘͠෯Λͨ͘͠ResNet
࣌ܥྻ 15
PyramidNet [Han+ CVPR 2017] 16 • DownsamplingΛ༻͍Δࡍͷٸܹͳ෯૿ՃʹΑΔਫ਼ྼԽΛ͙ͨΊɼ શମͰগͣͭ͠ͷ෯Λେ͖͘͢Δ
SENet [Hu+ CVPR 2018] 17 • ͷೖྗΛѹॖͨ͠ͷΛχϡʔϥϧωοτͰม͠ɼ͜ΕΛ༻͍ͯ ೖྗΛॏΈ͚Δ
DenseNet [Huang+ CVPR 2017 (best paper)] 18 • ֤ͦͷલͷͯ͢ͷͱskip connectionͰͭͳ͕Δ
MobileNet v1-3 [Howard+ 2017, Sandler+ 2018, Howard+ 2019] 19 •
ۭؒํͷΈͷΈࠐΉdepthwise convolutionͱ νϟωϧํͷΈΈࠐΉpointwise convolutionͰΈࠐΈͷܰྔԽ
PNASNet [Liu+ 2017] 20 • Neural architecture search (NAS)ͷ݁ՌಘΒΕͨϞσϧ •
CNNશମͰͳ͘ෳͷCNNϒϩοΫ͔ΒͳΔʮηϧʯΛ୳ࡧ • ୯७ͳͷ͔Βঃʑʹෳࡶͳͷͱ୳ࡧΛߦ͏
࣌ܥྻ 21
EfficientNet [Tan&Le ICML 2019] 22 • ͜Ε·Ͱͷ༷ʑͳϞσϧͷεέʔϧΞοϓख๏ͷશ෦ͷͤ
Noisy Student Training [Xie+ CVPR 2020] 23 • ֶशࡁΈੜెΛڭࢣͱͯ͠ɼॱ࣍େ͖ͳੜెΛֶश͢Δࣗݾڭࢣ͋Γֶश •
ੜెʹϊΠζΛՃ͢Δ͜ͱͰਫ਼ʹՃ͑ͯؤ݈ੑ্
BiT [Xie+ Kolesnikov 2019] 24 • 10ԯύϥϝʔλͷେنϞσϧͰࣄલֶश • సҠઌͷσʔλ͕গͳͯ͘͏·͍͘͘
࣌ܥྻ 25
Vision Transformer (ViT) [Dosovitskiy+ ICLR 2021] 26 • TransformerͰը૾ೝࣝͷSOTA
̎ɽ֤λεΫʹಛԽͨ͠Ϟσϧͷಈ 27
ମݕग़ 28
Ұൠମݕग़ 29 [https://pjreddie.com/media/files/papers/YOLOv3.pdf] • ը૾தͷମͷΫϥεͱҐஔΛͯΔ
࣌ܥྻ 30 [Zou+ 2020 Object Detection in 20 Years: A
Survey]
R-CNN [Girshick+ CVPR 2014] 31 • ΦϒδΣΫτ͕ଘࡏ͢ΔީิྖҬΛΓग़͠CNNͰಛநग़
Fast R-CNN [Girshick ICCV 2015] 32 • ·ͣը૾ͷಛϚοϓΛ࡞͠ɼީิྖҬ (ROI) ΛಛϚοϓ্ʹࣹӨ
• ΦϒδΣΫτͷྨͱόϯσΟϯάϘοΫεͷճؼNNͰߦ͏ • ֤ީิྖҬ͝ͱͰͳ֤͘ը૾͝ͱʹΈࠐΊΑ͘ͳΓɼߴԽ
Faster R-CNN [Ren+ NeurIPS 2015] 33 • ީิྖҬ (ROI) ͷఏҊ·ͰؚΊͯend-to-endʹֶश
YOLO v1-4 [Redmon+ CVPR 2016, CVPR 2017, 2018, Bochkovskiy+ 2020]
34 • ମݕग़ͱମࣝผΛҰؾ௨؏ʹߦ͏one-stageͷख๏ • Ϋϥε֬ɼ֬৴ɼόϯσΟϯάϘοΫεͷใΛग़ྗ
SSD [Liu+ ECCV 2016] 35 • YOLOಉ༷one-stageͷख๏ • ༧Ίෳ༻ҙͨ͠ͷόϯσΟϯάϘοΫεຖʹਪ •
֤ͷಛϚοϓ͔Βಛநग़͢Δ͜ͱͰ༷ʑͳεέʔϧͰମݕग़
RetinaNet [Lin+ ICCV 2017] 36 • ForegroundͱbackgroundͷΫϥεෆۉߧ͕one-stage๏͕ੑೳͰtwo- stage๏ʹྼΔཧ༝Ͱ͋Δ͜ͱΛࢦఠ • ΫϥεෆۉߧʹରԠ͢ΔͷͨΊͷFocal
LossͷఏҊʹΑΓɼ1-stageͳ ͕Βߴ͍ਫ਼ͷମೝࣝΛ࣮ݱ • ϕʔεͷΞʔΩςΫνϟʔʹޙड़ͷFeature Pyramid NetworkΛ༻
FCOS [Tian+ ICCV 2019] 37 • RetinaNetͷվྑ൛ • ମͷத৺ͷਪఆΛՃͰߦ͍ɼΞϯΧʔϑϦʔͳମݕग़Λ࣮ݱ
Bridging the Gap Between Anchor-based and Anchor-free Detection [Zhang+ 2019]
38 • Anchor-basedͱancho-freeͷҧ͍ɼෛྫͱਖ਼ྫͷબͷҧ͍
ηάϝϯςʔγϣϯ 39
ηάϝϯςʔγϣϯ 40 [https://arxiv.org/pdf/1706.05587.pdf] • ֤ϐΫηϧຖʹମͷΫϥε/എܠͷࣝผΛ͢Δ
࣌ܥྻ 41 [Minaee+ 2020 Image Segmentation Using Deep Learning: A
Survey]
FCN [Long+ CVPR 2015] 42 • CNNͷग़ྗΈࠐΈʹ͢Δ͜ͱͰɼώʔτϚοϓΛग़ྗ
SegNet [Badrinarayanan+ 2015] 43 • શͯΈࠐΈͷΤϯίʔμͱσίʔμ͔ΒͳΔωοτϫʔΫ • σίʔμΛ༻͍Δ͜ͱͰDeconvolutionஈ֊తʹߦ͑Δ
U-Net [Ronneberger+ MICCAI 2015] 44 • ΤϯίʔμͷಛදݱΛskip connectionͰσίʔμʹίϐʔͯ͢͠
DeepLab v1-3 [Chen+ TPAMI 2017] 45 • Down samplingΛͳ͘͠ɼdilated convolutionͱઢܗิؒΛΈ߹Θ
ͤΔ͜ͱͰߴղ૾ͳηάϝϯςʔγϣϯΛ࣮ݱ [Cui+ Remote Sens.2019]
FastFCN [Wu+ 2019] 46 • Joint Pyramid Upsampling (JPU) ͷಋೖͰdilated
convolutionʹൺͯ ܭࢉίετΛେ෯ʹݮ
Mask R-CNN [He+ ICCV 2017] 47 • Bounding boxͷ༧ଌʹՃ͑ͯΫϥεͷϚεΫ༧ଌ͢ΔFaster R-CNN
• RoIPoolʹΘΔRoIAlignͷಋೖͰྖҬׂͳͲՄೳʹ
PSPNet [Zhao+ CVPR 2017] 48 • ༷ʑͳεέʔϧͷϓʔϦϯάʹΑΓϚϧνεέʔϧͳಛදݱΛ֫ಘ
FPN [Lin+ CVPR 2017] 49 • CNNͷ֊ੑΛར༻֤͠֊Ͱ༧ଌͯ͠ϚϧνεέʔϧͳಛΛ֫ಘ • ग़ྗʹ͍ۙಛΛೖྗʹ͍ۙଆʹ͑Δ͜ͱͰɼઙ͍Ͱ༗ ҙຯͳಛநग़͕Մೳ
Visual Question Answering 50
Visual Question Answering 51 [https://arxiv.org/pdf/1505.00468.pdf] • ը૾ʹର͢Δ࣭จͷԠ
࣌ܥྻ 52 [Srivastava+ 2020 Visual Question Answering using Deep Learning:
A Survey and Performance Analysis]
σʔληοτ 53 [Srivastava+ 2020 Visual Question Answering using Deep Learning:
A Survey and Performance Analysis]
VQA [Agrawal+ ICCV 2015] 54 • LSTMͰ࣭จΛɼCNNͰը૾ΛຒΊࠐΜͰಛදݱΛ࡞
Stacked Attention Networks [Yang+ CVPR 2016] 55 • CNNಛྔʹଟஈ֊ͷattentionΛ͔͚ͯஈ֊తʹରΛߜΓࠐΉ
Embodied Question Answering [Das+ CVPR 2018] 56 • ࣭͕༩͑ΒΕΔͱɼΤʔδΣϯτγϛϡϨʔγϣϯۭؒͰߦಈ Λͱͬͯ͑Λݟ͚ͭΔ
CLEVR [Johnson+ CVPR 2017] 57 • VQAͷͨΊͷσʔληοτ • ཧతͳਪ͕ඞཁͱ͞ΕΔ
ಈըೝࣝ 58
࣌ܥྻ 59 [Zhu+ 2020 A Comprehensive Study of Deep Video
Action Recognition]
σʔληοτ 60 [Zhu+ 2020 A Comprehensive Study of Deep Video
Action Recognition]
ྨ 61 [Zhu+ 2020 A Comprehensive Study of Deep Video
Action Recognition]
3D CNN (C3D) [Tran+ ICCV 2015] 62 • 3࣍ݩΈࠐΈΛ༻͍Δ͜ͱͰ࣌ؒํͷಛදݱ
(2+1)D CNN [Tran+ CVPR 2018] 63 • ҰͭͷͰҰؾʹ࣌ؒํ·ͰΈࠐΉͷͰͳ͘ɼ·ۭͣؒํʹ ΈࠐΜͩ͋ͱͰ࣌ؒํʹΈࠐΉ
I3D [Carreira&Zisserman CVPR 2017] 64 • 3D ConvΛੵΈॏͶͨωοτϫʔΫ
Non-local [Wang+ CVPR 2018] 65 • AttentionʹΑΔॏΈ͚ͰɼେҬతͳใΛՃຯ • ͋ΔҐஔͷΛͦͷଞͷͯ͢ͷҐஔͷಛͷॏΈ͖Ͱදݱ
SlowFast Networks [Feichtenhofer+ ICCV 2019] 66 • ϑϨʔϜϨʔτͰۭؒಛΛɼߴϑϨʔϜϨʔτͰ࣌ؒಛΛଊ͑Δ
࢟ਪఆ 67
ྨ 68 [Chen+ 2020 Monocular Human Pose Estimation: A Survey
of Deep Learning-based Methods] [Zheng+ 2020 Deep Learning-Based Human Pose Estimation: A Survey]
Convolutional Pose Machines [Wei+ CVPR 2016] 69 • ଟஈ֊ͷ༧ଌʹΑΓɼ֤ମ෦Ґͷਪఆਫ਼ΛߴΊΔ
Part Affinity Fields [Cao+ CVPR 2017] 70 • ࢛ࢶͷҐஔͱ͖ΛຒΊࠐΉϕΫτϧΛ༻͍ͨ࢟ਪఆ
HRNet [Sun+ CVPR 2019] 71 • Sub-networkΛՃ͢Δ͜ͱͰશମͷղ૾Λམͱͣ࢟͞ਪఆ͕Մೳ
3D 72
ྨ 73 [Ahmed+ 2020 A survey on Deep Learning Advances
on Different 3D Data Representations]
3D ܈ 74
࣌ܥྻ 75 [Guo+ 2020 Deep Learning for 3D Point Clouds:
A Survey]
ྨ 76 [Guo+ 2020 Deep Learning for 3D Point Clouds:
A Survey]
σʔληοτ 77 [Guo+ 2020 Deep Learning for 3D Point Clouds:
A Survey]
PointNet [Qi+ CVPR 2017] 78 • ܈σʔλΛೖྗͱ͠ɼճసॱংͷมͳͲͷૢ࡞ʹରͯ͠ෆมͳಛ Λग़ྗ͢ΔωοτϫʔΫ
PointNet++ [Qi+ NeurIPS 2017] 79 • PointNetہॴతͳใΛ͏·͘र͍͑ͯͳ͔͕ͬͨɼPointNetΛ֊త ʹద༻͢Δ͜ͱͰ͜ΕʹରԠ
Dynamic Graph CNN [ACMTG+ 2019] 80 • ֤ͱͦͷۙͷؔΛදݱͨ͠ΤοδಛΛͭ͘ΔΈࠐΈͷఏҊ
VoxelNet [Zhou+ CVPR 2018] 81 • ܈σʔλΛvoxelʹΓ͚ɼ֤ϘΫηϧ୯ҐͰಛදݱͷຒΊࠐΈ • 3D܈ମೝࣝͷਫ਼্
3D ϝογϡ 82
Heat Diffusion Equation 83 • ۂ໘ʢϦʔϚϯଟ༷ମʣ্Ͱͷ֦ࢄΛߟ͑Δ [Bronstein+ 2016 Geometric deep
learning: going beyond Euclidean data]
Geodesic CNN [Masci+ ICCV 2015] 84 • ඇϢʔΫϦουଟ༷ମʹରԠՄೳͳCNNͷఏҊ • ֤Ͱۃ࠲ඪΛߟ͑Δ
Anisotropic CNN [Boscaini+ NeurIPS 2016] 85 • ඇํͳΧʔωϧΛߟ͑Δ͜ͱͰہॴతͳදݱΛΑΓΑ͘நग़ [Bronstein+ 2016
Geometric deep learning: going beyond Euclidean data]
Monet [Monti+ CVPR 2017] 86 • ͜Ε·ͰͷඇϢʔΫϦουCNNͷҰൠԽ • ࠲ඪͷҰൠԽ •
ݻఆͷΧʔωϧͰͳֶ͘शՄೳͳΧʔωϧΛ͍ɼΧʔωϧͷҰൠԽ
3D ඍՄೳϨϯμϥʔ 87
ඍՄೳϨϯμϥʔ 88 % % ϨϯμϦϯά
Perspective Transformer Nets [Yan+ NeurIPS 2016] 89 • ϘΫηϧͷඍՄೳϨϯμϥʔ
Neural 3D Mesh Renderer [Monti+ CVPR 2017] 90 • ߴਫ਼ͳϝογϡͷඍՄೳϨϯμϥʔ
• ϥελϥΠζ෦ΛඍՄೳʹͨ͜͠ͱͰٯՄೳʹ [https://www.slideshare.net/100001653434308/23d-neural-3d-mesh-renderer-cvpr-2018]
Transformers/Attention 91
࣌ܥྻ 92 [Han+ 2021 A Survey on Visual Transformer]
ྨ 93 [Han+ 2021 A Survey on Visual Transformer] [Khan+
2021 Transformers in Vision: A Survey]
DETR [Carion+ ECCV 2020] 94 • CNNͰը૾ಛΛநग़ͨ͠ͷͪɼtransformerͰମೝࣝ
iGPT [Chen+ ICML 2020] 95 • ը૾ಛΛGPT-2Ͱڭࢣͳֶ͠श
Vision Transformer (ViT) [Dosovitskiy+ ICLR 2021] 96 • ७ਮͳTransformerͰը૾ೝࣝͷSOTA ࠶ܝ
IPT [Chen+ 2020] 97 • ෳͷλεΫΛಉ࣌ʹߦ͏transformer
98 [https://twitter.com/jaguring1/status/1377710003377725441]
99 [https://www.slideshare.net/cvpaperchallenge/transformer-247407256]
ɽ·ͱΊ 100
·ͱΊ 101 • ϞσϧͷൃలResNetΛϕʔεʹɼෳࡶԽɾେنԽɾޮԽ • Vision transformer͕ଓʑొ • جຊతͳcomputer visionͷλεΫʹಛԽͨ͠ϞσϧϕϯνϚʔΫ͕
ݻ·͍ͬͯΔ༷ࢠ • 2D → 3DͷྲྀΕ • ϚϧνεέʔϧͳใͷΈࠐΈ͕Α͋͘Δҹ • ࡉ͔͍ςΫχοΫ͕ॏཁͳҹ [https://www.slideshare.net/cvpaperchallenge/cvpr-2020-237139930]
ࢀߟࢿྉͳͲ 102
ࢀߟࢿྉ 103 • [cvpaper.challenge-summary](https://github.com/hirokatsukataoka16/cvpaper.challenge-summary) • [CVPR 2016 ใ](https://www.slideshare.net/HirokatsuKataoka/cvpr-2016) • [CVPR
2017 ใ](https://www.slideshare.net/cvpaperchallenge/cvpr-2017-78294211) • [CVPR 2018 ใ](https://www.slideshare.net/cvpaperchallenge/cvpr-2018-102878612) • [CVPR 2019 ใ](https://www.slideshare.net/cvpaperchallenge/cvpr-2019) • [CVPR 2020 ใ](https://www.slideshare.net/cvpaperchallenge/cvpr-2020-237139930) • [ಈըೝࣝαʔϕΠv1ʢϝλαʔϕΠ ʣ](https://www.slideshare.net/cvpaperchallenge/v1-232973484) • [Vision and LanguageʢϝλαʔϕΠ ʣ](https://www.slideshare.net/cvpaperchallenge/vision-and-language-232926110) • [ΈࠐΈχϡʔϥϧωοτϫʔΫͷݚڀಈ](https://www.slideshare.net/ren4yu/ss-84282514) • [ConvNetͷྺ࢙ͱResNetѥछɺετϓϥΫςΟε](https://www.slideshare.net/ren4yu/convnetresnet) • [ΈࠐΈχϡʔϥϧωοτϫʔΫͷߴਫ਼ԽͱߴԽ](https://www.slideshare.net/ren4yu/ss-145689425) • [จհ: Fast R-CNN&Faster R-CNN](https://www.slideshare.net/takashiabe338/fast-rcnnfaster-rcnn) • [ʲମݕग़ʳSSD(Single Shot MultiBox Detector)ͷղઆ](https://www.acceluniverse.com/blog/developers/2020/02/SSD.html) • [ʲମݕग़ख๏ͷྺ࢙ : YOLOͷհʳ](https://qiita.com/cv_carnavi/items/68dcda71e90321574a2b) • [ը૾ೝࣝͱਂֶश](https://www.slideshare.net/ren4yu/ss-234439652) • [semantic segmentation αʔϕΠ](https://www.slideshare.net/yoheiokawa/semantic-segmentation-141471958) • [Semantic segmentation ৼΓฦΓ](https://speakerdeck.com/motokimura/semantic-segmentation-zhen-rifan-ri) • [[DLྠಡձ]SlowFast Networks for Video Recognition](https://www.slideshare.net/DeepLearningJP2016/dlslowfast-networks-for-video-recognition-202057397) • [ࡾ࣍ݩ܈ΛऔΓѻ͏χϡʔϥϧωοτϫʔΫͷαʔϕΠ](https://www.slideshare.net/naoyachiba18/ss-120302579) • [ࡾ࣍ݩ܈ΛऔΓѻ͏χϡʔϥϧωοτϫʔΫͷαʔϕΠ Ver. 2](https://speakerdeck.com/nnchiba/point-cloud-deep-learning-survey-ver-2) • [܈ਂֶश Meta-study](https://www.slideshare.net/naoyachiba18/metastudy) • [ୈ̍ճ ࠷৽ͷML,CV,NLP ؔ࿈จಡΈձ PointNet](https://www.slideshare.net/FujimotoKeisuke/point-net) • [ [DLྠಡձ]MeshͱDeep Learning Surface Networks & AtlasNet](https://www.slideshare.net/DeepLearningJP2016/dlmeshdeep-learning-surface-networks-atlasnet) • [จ·ͱΊɿConvolutional Pose Machines](https://qiita.com/masataka46/items/88f1a375ce8a485d9454) • [ίϯϐϡʔλϏδϣϯͷ࠷৽จௐࠪ 2D Human Pose Estimation ฤ](https://engineer.dena.com/posts/2019.11/cv-papers-19-2d-human-pose-estimation/) • [[ୈ2ճ3Dษڧձ ݚڀհ] Neural 3D Mesh Renderer (CVPR 2018)](https://www.slideshare.net/100001653434308/23d-neural-3d-mesh-renderer-cvpr-2018) • [DeepLabʹΘΓݱࡏͷSOTAͰ͋ΔFastFCN(JPU)ͷจղઆ](https://qiita.com/kamata1729/items/1b495658a63d76904ac3)
104