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CVPR 2020 報告

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cvpaper.challengeにおいてまとめた「CVPR 2020 報告」です。

cvpaper.challengeはコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ作成・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有します。2020の目標は「トップ会議に30+本投稿」することです。
http://xpaperchallenge.org/cv/

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CVPR 2020 報告

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  19. 19. /, CA • /, /6 18 – 9 618 • , 9 9 18 224 06 3 1 – 09 7 9 • , 9 9 18 224 06 3 1 https://www.youtube.com/watch?v=aHUYXtbwl_8
  20. 20. 1 V M bQ O • .= C H=PdaRT – Y ahSW e • /A H 0 • =CA A = 1 = A =CH – 8 I I:= gf c • HHE KKK IHI:= K H ? -=2. http://cvpr2020.thecvf.com/
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  29. 29. + P 8 • f e C ab V R 100/ /3 1 2/ /3 / / – : V R – 100/ /3 1 2/ /3 / / : e • d D Leveraging 2D Data to Learn Textured 3D Mesh Generation https://openaccess.thecvf.com/content_CVPR_2020/html/Henderso n_Leveraging_2D_Data_to_Learn_Textured_3D_Mesh_Generation _CVPR_2020_paper.html From Image Collections to Point Clouds With Self- Supervised Shape and Pose Networks https://openaccess.thecvf.com/content_CVPR_2020/html/Navane et_From_Image_Collections_to_Point_Clouds_With_Self- Supervised_Shape_and_CVPR_2020_paper.html
  30. 30. R P 2/ 0 • 8 8 V3 1 – : C D • C Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image hhttps://openaccess.thecvf.com/content_CVPR_2020/html/Pasc halidou_Learning_Unsupervised_Hierarchical_Part_Decomposit ion_of_3D_Objects_From_a_CVPR_2020_paper.html PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes https://openaccess.thecvf.com/content_CVPR_2020/papers/Wu _PQ- NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_ CVPR_2020_paper.pdf
  31. 31. bd cg D • mal P h 3 3: /083 3:23 3 – CW V CW R S f eiI • 3 3: /083 8 3 3:23 : 3/ : : 8 3 3 3: / : . 3 : • L L Colored Voxel http://www.krematas.com/nvr/index.html Neural Rendering Workshop https://www.neuralrender.com/
  32. 32. / W V I • NP R SN M : C 3 :G – 3 - 3 3 3 - 3- – - E F 0G C: 8 G 3 0 2 C: 3 G : C C: 3 D 3 3 3 – 0 F F 0G C: 2 C: D C 3 3 – 0G 0 C F 0G C: 8 3 0 3 Workshop https://nvlabs.github.io/nvs-tutorial-cvpr2020/ Link https://shihmengli.github.io/3D-Photo-Inpainting/
  33. 33. V e • h R D – 3 4/ 302 , 3 M d • 8 8 D i C – h h P P c D Articulation Implicit Function Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion https://openaccess.thecvf.com/content_CVPR_2020/html/Henderson_Leveraging_2D_Data_to_Learn_Textured_3 D_Mesh_Generation_CVPR_2020_paper.html
  34. 34. 3 / • C P R2 – C D C C P 0 D 5 82 3 3D Dynamic Voxel 3DV: 3D Dynamic Voxel for Action Recognition in Depth Video https://openaccess.thecvf.com/content_CVPR_2020/htm l/Wang_3DV_3D_Dynamic_Voxel_for_Action_Recogniti on_in_Depth_Video_CVPR_2020_paper.html
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  45. 45. 23 vyL P Twitter Strong Accept x3 https://twitter.com/doubledaibo/status/1232832762270236672 • v “ t L spv “ – .G pv “ pv “ P • ai b nS o spv zV P • g ce S l m – 4 D / 6 8D F D C P • R pv u rq R http://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fi ne-Grained_Action_Understanding_CVPR_2020_paper.html 068A D D 6 6 , 6 : 3 8 : H 4 D / 6 8D F D CJ 23
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  56. 56. • P e : – 04745/ 2 7 Ce O R • 28 4 a cC V i h • e V MixNMatch https://github.com/Yuheng- Li/MixNMatch Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis http://openaccess.thecvf.com/content_CVPR_ 2020/html/Liao_Towards_Unsupervised_Lear ning_of_Generative_Models_for_3D_Controlla ble_Image_CVPR_2020_paper.html Self-Supervised Scene De- Occlusion https://openaccess.thecvf.com/content_ CVPR_2020/html/Zhan_Self- Supervised_Scene_De- Occlusion_CVPR_2020_paper.html
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  68. 68. 8I • 2 25 / 7 0 – 2 25 0 2/05 L C – 2/05 7 0 9 • Visual-Textual Capsule Routing for Text-Based Video Segmentation Text Video Video Segmentation • Object Relational Graph With Teacher-Recommended Learning for Video Captioning (External language model dataset long-tail )
  69. 69. • 2 2 / 17 10 – 0 0 6086LR S C – 0 0 6086 P • Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection (Graph CN Shape Scene Text ) • SwapText:Image Based Texts Transfer in Scenes (Scene Text / )
  70. 70. , R • 6 / 1 10 – 86 / 1 P L – ,0 6 2 7 ,0 6 1G – 86 / 1 ,0 6 1 C V • REVERIE:Remote Embodied Visual Referring Expression in Real Indoor Environments (Grounding Embodied AI ) ( ) • SQulNTing at VQA Models:Introspecting VQA Models With Sub- Questions (Sub-questions Reasoning )( )
  71. 71. 23 n ou & • 8 1 A 9 – / 6 8 98 0 L 23 P V – 1 3 6 7 1 R I D – d iaI V C • Ecbegts • ca ts • mtsV
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  73. 73. 52 b P R • s y x b gS a – kpn rb w • 4F D8N .FIIc kpnbutnliW a f d V g jom b -D8 I yvR • .8 4:8C : : F 6 C FD -D 8C8 : / C .8 CI F 8:: II :L :FD :F 7 52 7 DC 2 7.8 4:8C 7 : 7 : F 7 7 76 C 7 FD7-D 8C8 : 7/ C .8 CI7 52 7 7 8 DC – b r • _ a h_ e b r • O 8D : D 0 MF F 8 I CO 28: : : F F 8:: II :L :FD :F 7 52 7 DC 28 7 O 8D :7 D 70 MF 7 F 7 78 7 I CO728: 7 : 7 : F 7 52 7 7 8 DC CVPR 2019 SKU-110K dataset https://github.com/eg4000/SKU110K_CVPR19
  74. 74. -6 5 ʼA D E • n p V – 88 L V /:7 8:7 tM V • C de r o N • F- 51 R C L P : 8 /:7 8:7 o /:7 /:7 2 789 0 88 51 -6 5 - 51 l
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  76. 76. 80 • 0 / V 0 – R – • 6 • • / W • C 7 – R P 2 7 6 2 2 S 79 6 7 6 6
  77. 77. 8 4 atvUu W X • K AL A R A L M LFMD K – cdkileg Y aflVh • 8 4 br sa D KM P MG L A • np b y_ L B L D I W 4 PALM AX .AIAL G D L • /IP A 6 GFb m w o – D KM P MG L A A L M IP A GFM KDK • AM KAL L – I D AID : 2AIAG M 0 I F M M .A LC G M D DAI , I I AG 8 I . G B L DA K KAL S, I CI M -A LA A LI IC B L / CA I 8 A MA - A I MK B ICT LAMAI A 8 4 A L M LFMD K https://vislab.ucr.edu/Biometrics2020/index.php
  78. 78. /6 d I UM( ) ) • 2 A: 289 A – a d 0k i J – I c i B BCd lrtIo stmd k vd k id e V a d k R d J P • d d 0d k – V dp n h i – d iRe c i – R k – i • /.d1 A: 8 28 Wd c C A: 89 A 9B https://people.eecs.berkeley.edu/~malik/
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