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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
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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
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http://www.krematas.com/nvr/index.html
Neural Rendering Workshop
https://www.neuralrender.com/
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https://nvlabs.github.io/nvs-tutorial-cvpr2020/
Link
https://shihmengli.github.io/3D-Photo-Inpainting/
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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
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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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A. Andonian*, C.Fosco*, ... & A.Oliva. We Have So Much in Common:
Modeling Relational Set Abstractions in Videos. [arXiv, soon]
Aude Oliva, MIT
http://ai.stanford.edu/~jingweij/cicv/
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https://twitter.com/doubledaibo/status/1232832762270236672
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ne-Grained_Action_Understanding_CVPR_2020_paper.html
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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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• 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 )
• 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 / )
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– 86 / 1 P L
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• 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 )( )
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CVPR 2020 報告

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  • 2. • 2 IRP 201 Ig c – e HI • 201 8 9 6 8 6 6 -9 : / : 4C • 2 8 9 6 8 6 6 -9 : / : 644C • 201 8 9 6 8 6 6 4C 6 48 6 76 4C • . 2 8 9 6 8 6 6 4C 6 48 6 76 944C • 201 8 9 6 8 6 6 4C 6 48 6 76 4C • 201 8 9 6 8 6 6 4C 6 48 6 76 4C – ik V 4C 6 48 6 76 H – H hKa I dEpl o np
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  • 6. o py • -9 6 tu sk – -9 6 u kSjl RcgR i vkT • ALLI AH L A L L L -9 6 LM P • ALLI C H I H N L – -9 6 thdR_S. 3 O 2H LP 8 A H H T • ALLI I D D H HLHD M NI IH L – -9 6 : V S 0 7-/41 6T • ALLI AH L A L L L NI : AH : – -9 6 x kSrjl ibeR a i skT • n ALLI D LH N H I H N L • m ALLI D LH N H I H N L
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  • 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. 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/
  • 21. 2/0 W kLhoR • . C ?8 ?: 0 C – bd mP O yta gz – T pc sa e v nrY O – iu1 C : 8 V w • 4 1 ?= C H 3 I/ 1 https://www.youtube.com/watch?v=aHUYXtbwl_8
  • 22. 12 cgIe # L • 1 0. /A - 1C H – a r MfPRlj – 12 TohS 12 lj – 33.. SV p ndi • ##7A : # 8 # # # 7# 7 7 C 1 https://www.youtube.com/watch?v=aHUYXtbwl_8
  • 23. I • 842 1 1 /31 0 – M – C AM P https://www.youtube.com/watch?v=aHUYXtbwl_8
  • 24. +2 / Q A C & • , @ +2 / – Q w eV lP s – m • 18 C0 4 18 5C n r h i • ZZ Q oRa 8 4 Q r HT S R d t https://www.youtube.com/watch?v=LkSBxpLOBx8
  • 25. 5 i S V W • L O hcY – t sT • h Uu U af h • v r nl fi fi Xo • hcY y p g k – U ws YT • l – 2L AL h Xb m_Y • 2KAI IC 0ARI A .i O O?Ae – D K QQQ R O O?A K R 1=?-P/ 8 O /? F QR 0 :?6:6/
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  • 29. . 89 i b • hjl(/ – (/ hycl i – “uxg o]sa efbd • )t v k1 1. [/FF 1FOFRBT VF P FMS[ / GGFRFOT BCMF 9FO FRFRhycl a m • (/ -FODINBRL F IB F FT .- • np ri F 3BPM O 2/2, A 8 PRDI(/ 0BDFCPPL A FSI 9 . 1 1L PWBR FT BM 2.. A Y OSU FRV SF FBRO O PG 8RPCBCM NNFTR D /FGPRNBCMF (/ 7CKFDTS GRPN 2NB FS O TIF M U FT BM . 89 A . 89 -FST 8B FR
  • 30. + 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
  • 31. 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
  • 32. 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/
  • 33. / 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/
  • 34. 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
  • 35. 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
  • 36. , Pb A IC • , E X – / no • / 0680 / • 06 8 22 8 0 6 6 08 0 82 083 62 – che pi r e ta • V Ps V P e uRTdFl I
  • 37. 2/0 e R • – c e • w e fbek d P • d i X pon • 2 e f x u dkn i ogb R E A A A Tna lon 3 3G I D8E , C A AE A G A . GD . I D E 3 A7 C D G A 2/0 rvs d n t e “ n ad km Vn N V e n be X d” W n h N
  • 38. e R Z V • 8 0 – jrul mtwd d • c e xa XSi “d • h e dch xsoviopnk h , AE K D P IG I EE KA F E FKA HHIG KG ,OHD AFAF F AF 2 I D 2 KNGIC 3 B K K KGI ” bekv wkd g i / F K D P0FK IHI K D F I K -AF I AF G FAKAGF A AGF .IG HAF
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  • 40. 0:78 j V • 2/91 – j2/91 j • 2/9 2/??9 e 4 =CA6ANj • zy j 9EI 4 =CAMj V V h s lbm V wtx R ERAG f h i n je io f V9 =G = 2A C M 2 AA =I j NN M, C M ?M=EG EN A PEME I 9EI 4 =CAM V s b j NN M, AI APEA IAN .E -M A T=/G3 4 4 =CA6ANj u v j V i a u v j c e h[f rb s V V V hg s hdb V4 =CA6AN 7 FA?N]pj NN , E =CA IAN C =NA MA
  • 41. 0 N n r A • 0 MC tMhe – ui • 3 1 2 32 3 /31 2 /AL E • n N ui Mo o V N lR P 44 2 38 3 MS A • N c a i fR
  • 42. : 7 jl uXS T • i xh f t x s – 4,8S1 27 T /AAD D L4 LS1 32 T faR • ec ognb S/AAD D L. LT j S .T rp • d V S /AAD D L. L .T 0 D CL C A L F O . /NI D , CDL LM A /AAD D L :D 7 DLD P : 7 3 9 L F O/AAD D L. L 8 F F /AAD D L E L . L LD P : 7
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  • 45. ,5 2 WkrMp ( ) O ( • k u M W t – ,CA C G C G ,5V k u W(dfWi • 3G C ,5 C Wm S ,CA C G C G h oS vn Wl VgS C C • /.4W 0 I se yW c c aWu : 8G I G 8 8 G C A G : :8C G C P Wu TR A. Andonian*, C.Fosco*, ... & A.Oliva. We Have So Much in Common: Modeling Relational Set Abstractions in Videos. [arXiv, soon] Aude Oliva, MIT http://ai.stanford.edu/~jingweij/cicv/
  • 46. 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
  • 47. 30 Xt u PM • t xfghr s r – , xfghrM • oVt R YXca y – - 8 : E 2C7 3 :A /A : DM • da np eiX s lW WR T oVs TP 4F :E 7 /F E C /:E A AC - 8 : E 2C7 3 :A /A : D 30 .: 8 E: A :C :E 7 , - 7 C8 E:8EFC:D AC - 8 : E 3 :A :8A E A 30
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  • 54. .412 P oImt aehgP o fr TR V – u v • u v AC C 78C 5 8 8 6 A5A8 6 5 8 :8 • 3 5 7C 18 9 5 4 78 286 : AC C 78C 5 8 8 6 A5A8 6 5 8 :8 5 7C A8 9 5 78 86 : – sxi • sxi AC C 78C 5 8 8 6 A5A8 6 5 8 :8 C8 9CEA8 C87 85 : – pl • .4Pwn -0P pl AC C 78C 5 8 8 6 A5A8 6 5 8 :8 5 – cd • -7 8 C5 5 / 5 A 8CaeP o AC C 78C 5 8 8 6 A5A8 6 5 8 :8 57 8 C5 5 8 5 A 8C
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  • 58. • 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
  • 59. 8 0 • P – 85 8 V/ – 8 V 2 – 8R 9 – V – C 48 5
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  • 68. ,:67 l Z • 0PI 5 G O O M OF - 0PI 7 – o beaih rd n i l s c x u gW s “ P O H , N A A 0PI 5 G O O M OF 7 D FOF V ,:67 t pW l s me j 8 8 FO O H 6 .P0- 3PHOF 2 H 6FR H HFD A ILHF FO .P OF C M 0FD 7 N HPOF - 0PI -FDFOFT OF V ,:67 leml j - ”yxY s c -:Z - -S IF : R H D O H -: - -S IF : R H C M OF 7 D FOF F - LO :FA V ,:67 l jv s fj pW i i l szY ibg h s P O H FH M O 6M AF OF D ,H O F D F - N .P OF C 0PI 6 N 8 L A / MI O 8OSH V ,:67
  • 69. 0 Fn • drs WR r PV – e io fh PVSm g – 5 96 /5 9 2 5 9 9 5 5 5D5C9D A: AD5D98 5 -5 9C • r lt PV H a – • dL cbc H 9 C90AC9
  • 70. 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 )
  • 71. • 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 / )
  • 72. , 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 )( )
  • 73. 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
  • 74. P 40 • – / I 67 – RX 0 / AC – P • 82 • P V 3 2
  • 75. 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
  • 76. -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
  • 77. 20 • p y - r RtP – n u a s c Pb • r jLg emilfh mR dt b 8 / 86E 7 687 6E A 86 D A AC EA A A D 28 6 8D O V d X : EA A A D C : I 8 C : 7 EA 7 2 D EE8 E A EE8 E A x oO v
  • 78.
  • 79. 80 • 0 / V 0 – R – • 6 • • / W • C 7 – R P 2 7 6 2 2 S 79 6 7 6 6
  • 80. 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
  • 81. /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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  • 131. / a a T (+U ( • s l k t s Sp • ls g – hl t S b R – g ] ] • nS St 0 ID 78 ) d dPv CD CN 3CI HH L F ) CN 8 IICACLDCL 52 -2 ) Pv S i o r 5 M 8 1C 2 8 2 -HFDI /CN CD k t s Sp at lg [ X g ] /CCK A ( CI 7-52 /FH NC I a V bs l k t s Sp g e
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  • 154. 166 tuW0 ckmd Z • t w s – t otie – p V rntv V Xhlgb a • t 1/ y Z 1 1 C C H TScaling Egocentric Vision: The EPIC-KITCHENS Dataset 200 E N- C A G AEC N D EP K 5 S C H T:EC 5 C AN 4P .A K BCK 1 NC - , E N- BCC B AK CNC AE K C NKP AC K C NKP AC B NC N G C AN 0 3P C H TAVA: A Video Dataset of Spatio- temporally Localized Atomic Visual Actions 0 78 E N- CNC AE DKKDHC AK BKR H K B E H M. Monfort et al. “Moments in Time Dataset: one million videos for event understanding,” in arXiv pre-print 1801.03150, 2018. http://moments.csail.mit.edu/ 4 E K C H THACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization C , E - E AN AN H CBP 9K C E D 9K C E D B NC E N- AK B NC N NK C E D NK C E D
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