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
computer-vision-survey
Search
KARAKURI Inc.
May 07, 2021
Research
3
480
computer-vision-survey
Computer Visionの近年の動向のサーベイ
KARAKURI Inc.
May 07, 2021
Tweet
Share
More Decks by KARAKURI Inc.
See All by KARAKURI Inc.
BERT-to-GPT Catch Up Survey
karakurist
2
2.2k
boke-generator
karakurist
2
490
user-behaviour-vol1
karakurist
3
400
user-behaviour-vol2
karakurist
4
830
nlp-survey
karakurist
24
3.7k
survey-imbalanced-learning
karakurist
7
1.9k
Other Decks in Research
See All in Research
[論文紹介] iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
shiba4839
0
150
VAGeo: View-specific Attention for Cross-View Object Geo-Localization
satai
3
220
A multimodal data fusion model for accurate and interpretable urban land use mapping with uncertainty analysis
satai
3
130
言語モデルの内部機序:解析と解釈
eumesy
PRO
38
16k
Security, Privacy, and Trust in Generative AI
tsubasashi
0
120
JSAI NeurIPS 2024 参加報告会(AI アライメント)
akifumi_wachi
5
980
インドネシアのQA事情を紹介するの
yujijs
0
190
eAI (Engineerable AI) プロジェクトの全体像 / Overview of eAI Project
ishikawafyu
0
460
Data-centric AI勉強会 「ロボットにおけるData-centric AI」
haraduka
0
580
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
satai
3
130
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
satai
3
130
公立高校入試等に対する受入保留アルゴリズム(DA)導入の提言
shunyanoda
0
3.1k
Featured
See All Featured
Statistics for Hackers
jakevdp
799
220k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
Into the Great Unknown - MozCon
thekraken
38
1.7k
Why You Should Never Use an ORM
jnunemaker
PRO
56
9.3k
Six Lessons from altMBA
skipperchong
28
3.8k
The Cult of Friendly URLs
andyhume
78
6.3k
Designing for Performance
lara
608
69k
Java REST API Framework Comparison - PWX 2021
mraible
31
8.6k
A designer walks into a library…
pauljervisheath
205
24k
Designing for humans not robots
tammielis
253
25k
Stop Working from a Prison Cell
hatefulcrawdad
268
20k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
507
140k
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