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computer-vision-survey

 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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  1. Computer visionͷۙ೥ͷಈ޲ͷαʔϕΠ
    ߴ໦ࢤ࿠
    1

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  2. αʔϕΠͷ໨త
    2
    Computer vision (CV) ݚڀͷۙ೥ͷಈ޲Λ஌Γ͍ͨʂ
    • ֶशख๏Λ஌Γ͍ͨ
    • ωοτϫʔΫͷมભΛ஌Γ͍ͨ
    ˠ χϡʔϥϧҎ߱ͷ$7ͷมભ΍͜Ε·Ͱͷಈ޲Λ޿͘ઙ͘঺հ

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  3. ࠓճ͸࿩͞ͳ͍͜ͱ
    3
    • ը૾/ಈըੜ੒Ұൠ
    • ఢରతֶश
    • ൒ڭࢣ͋Γֶश
    • ࣗݾڭࢣ͋Γֶश
    • ݹయతͳίϯϐϡʔλʔϏδϣϯ
    ͳͲͳͲɽɽ

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  4. ࠓ೔ͷྲྀΕ
    4
    ̍ɽλεΫඇಛԽϞσϧʢը૾ೝࣝͷϞσϧʣͷಈ޲
    ̎ɽ֤λεΫʹಛԽͨ͠Ϟσϧͷಈ޲
    ̏ɽ·ͱΊ

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  5. ͦͷલʹ
    5
    ɾਆࢿྉ܈
    ɾͪ͜ΒͷࢿྉΛେ͍ʹࢀߟʹ͠·ͨ͠
    http://xpaperchallenge.org/cv/
    https://github.com/hirokatsukataoka16/cvpaper.challenge-summary

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  6. ̍ɽλεΫඇಛԽϞσϧͷಈ޲
    6

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  7. ΞʔΩςΫνϟɾֶश๏ʢը૾ೝࣝʣ
    7

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  8. ࣌ܥྻ
    8


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  9. AlexNet [Krizhevsky+ NeurIPS 2012]
    9
    • ը૾ೝࣝίϯϖͰ͋ΔILSVRC2012Ͱѹউ
    • ਂ૚৞ΈࠐΈχϡʔϥϧωοτϫʔΫ(CNN)ͷ࣌୅ͷນ։͚

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  10. ࣌ܥྻ
    10


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  11. ResNet [He+ CVPR 2016]
    11
    • ILSVRC2015༏উϞσϧ
    • Skip connectionͷಋೖͰ152૚΋ͷ௒ਂ૚CNNͷֶश͕Մೳʹ
    • Ҏ߱ͷը૾ೝࣝͷϞσϧ͸جຊతʹResNetͷվྑ

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  12. ࣌ܥྻ
    12


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  13. ResNext [Xie+ CVPR 2017]
    13
    • ೖྗΛ෼ذͤͯ͞ෳ਺ͷωοτϫʔΫͰॲཧ͠ɼͦͷ݁ՌΛ଍͠߹ΘͤΔ

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  14. WideResNet [Zagoruyko+ 2017]
    14
    • ਂ͞Λઙͯ͘͠෯Λ޿ͨ͘͠ResNet

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  15. ࣌ܥྻ
    15


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  16. PyramidNet [Han+ CVPR 2017]
    16
    • DownsamplingΛ༻͍Δࡍͷٸܹͳ૚෯૿ՃʹΑΔਫ਼౓ྼԽΛ๷͙ͨΊɼ
    શମͰগͣͭ͠૚ͷ෯Λେ͖͘͢Δ

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  17. SENet [Hu+ CVPR 2018]
    17
    • ૚΁ͷೖྗΛѹॖͨ͠΋ͷΛχϡʔϥϧωοτͰม׵͠ɼ͜ΕΛ༻͍ͯ
    ೖྗΛॏΈ෇͚Δ

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  18. DenseNet [Huang+ CVPR 2017 (best paper)]
    18
    • ֤૚͸ͦͷલͷ͢΂ͯͷ૚ͱskip connectionͰͭͳ͕Δ

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  19. MobileNet v1-3 [Howard+ 2017, Sandler+ 2018, Howard+ 2019]
    19
    • ۭؒํ޲ͷΈͷ৞ΈࠐΉdepthwise convolutionͱ
    νϟωϧํ޲ͷΈ৞ΈࠐΉpointwise convolutionͰ৞ΈࠐΈͷܰྔԽ

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  20. PNASNet [Liu+ 2017]
    20
    • Neural architecture search (NAS)ͷ݁ՌಘΒΕͨϞσϧ
    • CNNશମͰ͸ͳ͘ෳ਺ͷCNNϒϩοΫ͔ΒͳΔʮηϧʯΛ୳ࡧ
    • ୯७ͳ΋ͷ͔Βঃʑʹෳࡶͳ΋ͷ΁ͱ୳ࡧΛߦ͏

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  21. ࣌ܥྻ
    21


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  22. EfficientNet [Tan&Le ICML 2019]
    22
    • ͜Ε·Ͱͷ༷ʑͳϞσϧͷεέʔϧΞοϓख๏ͷશ෦ͷͤ

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  23. Noisy Student Training [Xie+ CVPR 2020]
    23
    • ֶशࡁΈੜెΛڭࢣͱͯ͠ɼॱ࣍େ͖ͳੜెΛֶश͢Δࣗݾڭࢣ͋Γֶश
    • ੜెʹϊΠζΛ෇Ճ͢Δ͜ͱͰਫ਼౓ʹՃ͑ͯؤ݈ੑ΋޲্

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  24. BiT [Xie+ Kolesnikov 2019]
    24
    • ໿10ԯύϥϝʔλͷ௒େن໛ϞσϧͰࣄલֶश
    • సҠઌͷσʔλ͕গͳͯ͘΋͏·͍͘͘

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  25. ࣌ܥྻ
    25


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  26. Vision Transformer (ViT) [Dosovitskiy+ ICLR 2021]
    26
    • TransformerͰը૾ೝࣝͷSOTA

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  27. ̎ɽ֤λεΫʹಛԽͨ͠Ϟσϧͷಈ޲
    27

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  28. ෺ମݕग़
    28

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  29. Ұൠ෺ମݕग़
    29
    [https://pjreddie.com/media/files/papers/YOLOv3.pdf]
    • ը૾தͷ෺ମͷΫϥεͱҐஔΛ౰ͯΔ

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  30. ࣌ܥྻ
    30
    [Zou+ 2020 Object Detection in 20 Years: A Survey]

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  31. R-CNN [Girshick+ CVPR 2014]
    31
    • ΦϒδΣΫτ͕ଘࡏ͢ΔީิྖҬΛ੾Γग़͠CNNͰಛ௃நग़

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  32. Fast R-CNN [Girshick ICCV 2015]
    32
    • ·ͣը૾ͷಛ௃ϚοϓΛ࡞੒͠ɼީิྖҬ (ROI) Λಛ௃Ϛοϓ্ʹࣹӨ
    • ΦϒδΣΫτͷ෼ྨͱό΢ϯσΟϯάϘοΫεͷճؼ΋NNͰߦ͏
    • ֤ީิྖҬ͝ͱͰ͸ͳ֤͘ը૾͝ͱʹ৞ΈࠐΊ͹Α͘ͳΓɼߴ଎Խ

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  33. Faster R-CNN [Ren+ NeurIPS 2015]
    33
    • ީิྖҬ (ROI) ͷఏҊ·ͰؚΊͯend-to-endʹֶश

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  34. YOLO v1-4
    [Redmon+ CVPR 2016, CVPR 2017, 2018, Bochkovskiy+ 2020]
    34
    • ෺ମݕग़ͱ෺ମࣝผΛҰؾ௨؏ʹߦ͏one-stageͷख๏
    • Ϋϥε֬཰ɼ֬৴౓ɼό΢ϯσΟϯάϘοΫεͷ৘ใΛग़ྗ

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  35. SSD [Liu+ ECCV 2016]
    35
    • YOLOಉ༷one-stageͷख๏
    • ༧Ίෳ༻ҙͨ͠਺ͷό΢ϯσΟϯάϘοΫεຖʹਪ࿦
    • ֤૚ͷಛ௃Ϛοϓ͔Βಛ௃நग़͢Δ͜ͱͰ༷ʑͳεέʔϧͰ෺ମݕग़

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  36. RetinaNet [Lin+ ICCV 2017]
    36
    • ForegroundͱbackgroundͷΫϥεෆۉߧ͕one-stage๏͕ੑೳͰtwo-
    stage๏ʹྼΔཧ༝Ͱ͋Δ͜ͱΛࢦఠ
    • ΫϥεෆۉߧʹରԠ͢ΔͷͨΊͷFocal LossͷఏҊʹΑΓɼ1-stageͳ
    ͕Βߴ͍ਫ਼౓ͷ෺ମೝࣝΛ࣮ݱ
    • ϕʔεͷΞʔΩςΫνϟʔʹޙड़ͷFeature Pyramid NetworkΛ࢖༻

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  37. FCOS [Tian+ ICCV 2019]
    37
    • RetinaNetͷվྑ൛
    • ෺ମͷத৺ͷਪఆΛ௥ՃͰߦ͍ɼΞϯΧʔϑϦʔͳ෺ମݕग़Λ࣮ݱ

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  38. Bridging the Gap Between Anchor-based and
    Anchor-free Detection [Zhang+ 2019]
    38
    • Anchor-basedͱancho-freeͷҧ͍͸ɼෛྫͱਖ਼ྫͷબ୒ͷҧ͍

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  39. ηάϝϯςʔγϣϯ
    39

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  40. ηάϝϯςʔγϣϯ
    40
    [https://arxiv.org/pdf/1706.05587.pdf]
    • ֤ϐΫηϧຖʹ෺ମͷΫϥε/എܠͷࣝผΛ͢Δ

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  41. ࣌ܥྻ
    41
    [Minaee+ 2020 Image Segmentation Using Deep Learning: A Survey]

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  42. FCN [Long+ CVPR 2015]
    42
    • CNNͷग़ྗ૚΋৞ΈࠐΈ૚ʹ͢Δ͜ͱͰɼώʔτϚοϓΛग़ྗ

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  43. SegNet [Badrinarayanan+ 2015]
    43
    • શͯ৞ΈࠐΈ૚ͷΤϯίʔμͱσίʔμ͔ΒͳΔωοτϫʔΫ
    • σίʔμΛ༻͍Δ͜ͱͰDeconvolution΋ஈ֊తʹߦ͑Δ

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  44. U-Net [Ronneberger+ MICCAI 2015]
    44
    • Τϯίʔμͷಛ௃දݱΛskip connectionͰσίʔμʹίϐʔͯ͠౉͢

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  45. DeepLab v1-3 [Chen+ TPAMI 2017]
    45
    • Down samplingΛͳ͘͠ɼdilated convolutionͱ૒ઢܗิؒΛ૊Έ߹Θ
    ͤΔ͜ͱͰߴղ૾౓ͳηάϝϯςʔγϣϯΛ࣮ݱ
    [Cui+ Remote Sens.2019]

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  46. FastFCN [Wu+ 2019]
    46
    • Joint Pyramid Upsampling (JPU) ͷಋೖͰdilated convolutionʹൺ΂ͯ
    ܭࢉίετΛେ෯ʹ࡟ݮ

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  47. Mask R-CNN [He+ ICCV 2017]
    47
    • Bounding boxͷ༧ଌʹՃ͑ͯΫϥεͷϚεΫ΋༧ଌ͢ΔFaster R-CNN
    • RoIPoolʹ୅ΘΔRoIAlignͷಋೖͰྖҬ෼ׂͳͲ΋Մೳʹ

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  48. PSPNet [Zhao+ CVPR 2017]
    48
    • ༷ʑͳεέʔϧͷϓʔϦϯάʹΑΓϚϧνεέʔϧͳಛ௃දݱΛ֫ಘ

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  49. FPN [Lin+ CVPR 2017]
    49
    • CNNͷ֊૚ੑΛར༻֤͠֊૚Ͱ༧ଌͯ͠Ϛϧνεέʔϧͳಛ௃Λ֫ಘ
    • ग़ྗ૚ʹ͍ۙಛ௃Λೖྗ૚ʹ͍ۙଆʹ΋఻͑Δ͜ͱͰɼઙ͍૚Ͱ΋༗
    ҙຯͳಛ௃நग़͕Մೳ

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  50. Visual Question Answering
    50

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  51. Visual Question Answering
    51
    [https://arxiv.org/pdf/1505.00468.pdf]
    • ը૾ʹର͢Δ࣭໰จ΁ͷԠ౴

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  52. ࣌ܥྻ
    52
    [Srivastava+ 2020 Visual Question Answering using Deep Learning: A Survey and Performance Analysis]

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  53. σʔληοτ
    53
    [Srivastava+ 2020 Visual Question Answering using Deep Learning: A Survey and Performance Analysis]

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  54. VQA [Agrawal+ ICCV 2015]
    54
    • LSTMͰ࣭໰จΛɼCNNͰը૾ΛຒΊࠐΜͰಛ௃දݱΛ࡞੒

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  55. Stacked Attention Networks [Yang+ CVPR 2016]
    55
    • CNNಛ௃ྔʹଟஈ֊ͷattentionΛ͔͚ͯஈ֊తʹର৅ΛߜΓࠐΉ

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  56. Embodied Question Answering [Das+ CVPR 2018]
    56
    • ࣭໰͕༩͑ΒΕΔͱɼΤʔδΣϯτ͸γϛϡϨʔγϣϯۭؒ಺Ͱߦಈ
    Λͱͬͯ౴͑Λݟ͚ͭΔ

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  57. CLEVR [Johnson+ CVPR 2017]
    57
    • VQAͷͨΊͷσʔληοτ
    • ࿦ཧతͳਪ࿦͕ඞཁͱ͞ΕΔ

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  58. ಈըೝࣝ
    58

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  59. ࣌ܥྻ
    59
    [Zhu+ 2020 A Comprehensive Study of Deep Video Action Recognition]

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  60. σʔληοτ
    60
    [Zhu+ 2020 A Comprehensive Study of Deep Video Action Recognition]

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  61. ෼ྨ
    61
    [Zhu+ 2020 A Comprehensive Study of Deep Video Action Recognition]

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  62. 3D CNN (C3D) [Tran+ ICCV 2015]
    62
    • 3࣍ݩ৞ΈࠐΈΛ༻͍Δ͜ͱͰ࣌ؒํ޲ͷಛ௃΋දݱ

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  63. (2+1)D CNN [Tran+ CVPR 2018]
    63
    • Ұͭͷ૚ͰҰؾʹ࣌ؒํ޲·Ͱ৞ΈࠐΉͷͰ͸ͳ͘ɼ·ۭͣؒํ޲ʹ
    ৞ΈࠐΜͩ͋ͱͰ࣌ؒํ޲ʹ৞ΈࠐΉ

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  64. I3D [Carreira&Zisserman CVPR 2017]
    64
    • 3D ConvΛੵΈॏͶͨωοτϫʔΫ

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  65. Non-local [Wang+ CVPR 2018]
    65
    • AttentionʹΑΔॏΈ෇͚ͰɼେҬతͳ৘ใΛՃຯ
    • ͋ΔҐஔͷ஋Λͦͷଞͷ͢΂ͯͷҐஔͷಛ௃ͷॏΈ෇͖࿨Ͱදݱ

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  66. SlowFast Networks [Feichtenhofer+ ICCV 2019]
    66
    • ௿ϑϨʔϜϨʔτͰۭؒಛ௃ΛɼߴϑϨʔϜϨʔτͰ࣌ؒಛ௃Λଊ͑Δ

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  67. ࢟੎ਪఆ
    67

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  68. ෼ྨ
    68
    [Chen+ 2020 Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods]
    [Zheng+ 2020 Deep Learning-Based Human Pose Estimation: A Survey]

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  69. Convolutional Pose Machines [Wei+ CVPR 2016]
    69
    • ଟஈ֊ͷ༧ଌʹΑΓɼ֤਎ମ෦Ґͷਪఆਫ਼౓ΛߴΊΔ

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  70. Part Affinity Fields [Cao+ CVPR 2017]
    70
    • ࢛ࢶͷҐஔͱ޲͖ΛຒΊࠐΉϕΫτϧ৔Λ༻͍ͨ࢟੎ਪఆ

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  71. HRNet [Sun+ CVPR 2019]
    71
    • Sub-networkΛ௥Ճ͢Δ͜ͱͰશମͷղ૾౓Λམͱͣ࢟͞੎ਪఆ͕Մೳ

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  72. 3D
    72

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  73. ෼ྨ
    73
    [Ahmed+ 2020 A survey on Deep Learning Advances on Different 3D Data Representations]

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  74. 3D ఺܈

    74

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  75. ࣌ܥྻ
    75
    [Guo+ 2020 Deep Learning for 3D Point Clouds: A Survey]

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  76. ෼ྨ
    76
    [Guo+ 2020 Deep Learning for 3D Point Clouds: A Survey]

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  77. σʔληοτ
    77
    [Guo+ 2020 Deep Learning for 3D Point Clouds: A Survey]

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  78. PointNet [Qi+ CVPR 2017]
    78
    • ఺܈σʔλΛೖྗͱ͠ɼճస΍ॱংͷม׵ͳͲͷૢ࡞ʹରͯ͠ෆมͳಛ
    ௃Λग़ྗ͢ΔωοτϫʔΫ

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  79. PointNet++ [Qi+ NeurIPS 2017]
    79
    • PointNet͸ہॴతͳ৘ใΛ͏·͘र͍͑ͯͳ͔͕ͬͨɼPointNetΛ֊૚త
    ʹద༻͢Δ͜ͱͰ͜ΕʹରԠ

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  80. Dynamic Graph CNN [ACMTG+ 2019]
    80
    • ֤఺ͱͦͷۙ๣ͷؔ܎Λදݱͨ͠Τοδಛ௃Λͭ͘Δ৞ΈࠐΈͷఏҊ

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  81. VoxelNet [Zhou+ CVPR 2018]
    81
    • ఺܈σʔλΛvoxelʹ੾Γ෼͚ɼ֤ϘΫηϧ୯ҐͰಛ௃දݱͷຒΊࠐΈ
    • 3D఺܈෺ମೝࣝͷਫ਼౓޲্

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  82. 3D ϝογϡ

    82

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  83. Heat Diffusion Equation
    83
    • ۂ໘ʢϦʔϚϯଟ༷ମʣ্Ͱͷ೤֦ࢄΛߟ͑Δ
    [Bronstein+ 2016 Geometric deep learning: going beyond Euclidean data]

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  84. Geodesic CNN [Masci+ ICCV 2015]
    84
    • ඇϢʔΫϦουଟ༷ମʹ΋ରԠՄೳͳCNNͷఏҊ
    • ֤఺Ͱۃ࠲ඪΛߟ͑Δ

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  85. Anisotropic CNN [Boscaini+ NeurIPS 2016]
    85
    • ඇ౳ํͳ೤ΧʔωϧΛߟ͑Δ͜ͱͰہॴతͳදݱΛΑΓΑ͘நग़
    [Bronstein+ 2016 Geometric deep learning: going beyond Euclidean data]

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  86. Monet [Monti+ CVPR 2017]
    86
    • ͜Ε·ͰͷඇϢʔΫϦουCNNͷҰൠԽ
    • ࠲ඪͷҰൠԽ
    • ݻఆͷΧʔωϧͰ͸ͳֶ͘शՄೳͳΧʔωϧΛ࢖͍ɼΧʔωϧͷҰൠԽ

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  87. 3D ඍ෼ՄೳϨϯμϥʔ

    87

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  88. ඍ෼ՄೳϨϯμϥʔ
    88
    %
    %
    ϨϯμϦϯά

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  89. Perspective Transformer Nets [Yan+ NeurIPS 2016]
    89
    • ϘΫηϧͷඍ෼ՄೳϨϯμϥʔ

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  90. Neural 3D Mesh Renderer [Monti+ CVPR 2017]
    90
    • ߴਫ਼౓ͳϝογϡͷඍ෼ՄೳϨϯμϥʔ
    • ϥελϥΠζ෦෼Λඍ෼Մೳʹͨ͜͠ͱͰٯ఻೻Մೳʹ
    [https://www.slideshare.net/100001653434308/23d-neural-3d-mesh-renderer-cvpr-2018]

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  91. Transformers/Attention
    91

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  92. ࣌ܥྻ
    92
    [Han+ 2021 A Survey on Visual Transformer]

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  93. ෼ྨ
    93
    [Han+ 2021 A Survey on Visual Transformer]
    [Khan+ 2021 Transformers in Vision: A Survey]

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  94. DETR [Carion+ ECCV 2020]
    94
    • CNNͰը૾ಛ௃Λநग़ͨ͠ͷͪɼtransformerͰ෺ମೝࣝ

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  95. iGPT [Chen+ ICML 2020]
    95
    • ը૾ಛ௃ΛGPT-2Ͱڭࢣͳֶ͠श

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  96. Vision Transformer (ViT) [Dosovitskiy+ ICLR 2021]
    96
    • ७ਮͳTransformerͰը૾ೝࣝͷSOTA
    ࠶ܝ

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  97. IPT [Chen+ 2020]
    97
    • ෳ਺ͷλεΫΛಉ࣌ʹߦ͏transformer

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  98. 98
    [https://twitter.com/jaguring1/status/1377710003377725441]

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  99. 99
    [https://www.slideshare.net/cvpaperchallenge/transformer-247407256]

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  100. ɽ·ͱΊ
    100

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  101. ·ͱΊ
    101
    • Ϟσϧͷൃల͸ResNetΛϕʔεʹɼෳࡶԽɾେن໛Խɾޮ཰Խ
    • Vision transformer͕ଓʑొ৔
    • جຊతͳcomputer visionͷλεΫʹಛԽͨ͠Ϟσϧ͸ϕϯνϚʔΫ͕
    ݻ·͍ͬͯΔ༷ࢠ
    • 2D → 3DͷྲྀΕ
    • Ϛϧνεέʔϧͳ৘ใͷ૊ΈࠐΈ͕Α͋͘Δҹ৅
    • ࡉ͔͍ςΫχοΫ͕ॏཁͳҹ৅
    [https://www.slideshare.net/cvpaperchallenge/cvpr-2020-237139930]

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  102. ࢀߟࢿྉͳͲ
    102

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  103. 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)
    • [ConvNetͷྺ࢙ͱResNetѥछɺ΂ετϓϥΫςΟε](https://www.slideshare.net/ren4yu/convnetresnet)
    • [৞ΈࠐΈχϡʔϥϧωοτϫʔΫͷߴਫ਼౓Խͱߴ଎Խ](https://www.slideshare.net/ren4yu/ss-145689425)
    • [࿦จ঺հ: Fast R-CNN&Faster R-CNN](https://www.slideshare.net/takashiabe338/fast-rcnnfaster-rcnn)
    • [ʲ෺ମݕग़ʳSSD(Single Shot MultiBox Detector)ͷղઆ](https://www.acceluniverse.com/blog/developers/2020/02/SSD.html)
    • [ʲ෺ମݕग़ख๏ͷྺ࢙ : YOLOͷ঺հʳ](https://qiita.com/cv_carnavi/items/68dcda71e90321574a2b)
    • [ը૾ೝࣝͱਂ૚ֶश](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 ݚڀ঺հ] Neural 3D Mesh Renderer (CVPR 2018)](https://www.slideshare.net/100001653434308/23d-neural-3d-mesh-renderer-cvpr-2018)
    • [DeepLabʹ୅ΘΓݱࡏͷSOTAͰ͋ΔFastFCN(JPU)ͷ࿦จղઆ](https://qiita.com/kamata1729/items/1b495658a63d76904ac3)

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  104. 104

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