3. x
• ms urV x dkleV
– ms
• . Vdkle
• t R
• Vv .Vdkle
– w V No p
•
– ah . a g_
• C 0 3 1 0883: 3 1 3D 1 / 0 3 8 088
• y m nV
– d c u Vi a g_ P
• 8 23 0 3 :3 1 0 3 1 0883: 3 3 3: 0 :
• 70 70 0 70 :3 C 0 3 1 0883: 3 8
4. EUPCPG EIC GNHG/
• i2A o bnu t
◆ j l h nje m
T UG GMDG .
GRGC EI GMDG . +
i
j l
dBGD hd
i?
2A W 82 0W 1 A2 822AB 2A B 422AB 82 W
2A
-
u
t
422A
-
s yp
EUPCPG EIC GNHGi x t rv
2A i o a v wo n
o g
422A +B 0VC F
A 4B +
3 NCM E 5CRI ON 2T ST GR
8 ,
#
NIDB
Near-Miss Incident DB
82 0 - 2A -
3 GR:GSR ?O EI
2A - 6 S7TD
ci i O
&
/
HP: http://xpaperchallenge.org/
Twitter: @CVpaperChalleng
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
7. e
,
• 1 67 hbcdUja
– 1 67 hbcdV1R A /CAI T /4 A D O W
• D . R A CAI G C A A D O A P I (
– 1 67 3 CGA igfV A 0 C2 W
• D . A C
– 1 67 :I A R 0A 6 A /II OI A RI A
• D . RI A AP A P
OI A R A A II OI A
– 1 67 . 9DA 9 5 A 2A A I 6 A
• D . DA A B S P DA A
A A I A B- A) ((
8. bad
• 1/0 D 25 @
– 1 /@ 5 A R IP
– 1/0 F 2 2 C G
• 8 A 8C 2 2 C D 25 1/0
– c V H I . 1 F 2:282A8 C G
• 8 A 8C 2:282A8 C D 25. 1
10. 2 / Nin G C
• 2 / eIST A
– 5 0 3 o r a h Oo r
• ,3 5 rC 0 85 5 rC 5 rCNl tP
• 2O c N VA 2 Sg u F
https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=en
1 O R
11. 1./ c CeiI
• 1./ 19> 2:
– dP I fabp V bp
• -1 Smo
• O fa V >9 bp hl
• 1./ . > 2: VD
1./ P0 2 : HR19> 2: 8 > 8
12. 3/0 U c bh
• 3/0 3 C 7=
– l V
• U XVe
• a_ YH i ? 1 8:Ra_P Y T Ye
CC ? C 8: 9? 7C9 , +7.2 C8 =
13. 3856 % %Vc iR ) P
(
• 3856 % % 8A H
– ho l ) , l % P
• Y Y 23 . _ ema( )_Up
• 23 _ (% X b
HH / H =? C H 1 0 47:9H=
14. .:5 & & c tVs X &Y
• .:5 & & :FLN
– } cghf
• D FNF X ow U {ruY 4
• 1-80a m lnd e_v
• yic/ L F Dghf 8L C L E N
S HF T LPF A L F D 0CCF F N NL F F D F C L
L N NF L F D -AP L LF L F D 4 L D L NFP
H A b lp
ENN R N ? H N E,P 2 N? =
16. / 23 Xf ip P #
%
• / 23 G 9
– Xaw
• %m P m d P
• nRV m u P Y% V m H = = Yv
• rsgbU Yty e 0 ? = _loc e h
H,## := C# 9 . 91 76 : 8
17. 0856 hl o ,#, U
%
• 0856 8 M F
– kwc dv
• %m f UR m b U
• nV m t , UR a m 7H ? uX
• rieY 2 F 1 = s _g 047 f p
CMM ##PPP M = G#P M C/ . 37: M=PF ,
18. 3 9: & &r“ ea % b
• 3 9: & & TVW N
– y qpk fon]? 2 d l
• 3 W WE V a r“ _b.
VVRU.%%YYY WVWE F O%F PP N%=3&P F TU7C ?Y Y
• 3 8R P 1FF UU A3 9: & & 6 P 3 P T PF B.
VVR.%% R P FF UU V F F O%3 9: & & R
• 3 8R P 1FF UU A3 9: & & ? TMU RB.
VVR.%% R P FF UU V F F O%3 9: & &CY TMU RU%O PW R
• Y VV T [ 3 9: & & .
VVRU.%%VY VV T F O%U TF 0S/ 3 9: & & UTF/V R CSW T /N
– t w r“ shviv gvmcu_
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/
26. . % % 0 %5
%
• %
– RV 2
• 6 27 P V
8 P6 7 6 /
17
–
• V /
• V 5 P C P
27.
28. *2/0 lr nwM I
•
– 3 C3 / 1AB A /IV N da
• C3 t f ae
– x u p c hM N
C3
i s P 8 / A* B 2 D , A1B 3 SRt Smo
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
39. 2
• 3,
– . Zi
• V R R L
• .c a a R L
• I d P a
tMr uml Zi a
TR I V
R R R fI
Io
t ns p
/ 9E 99B C FDE9C
AC DF /9 C 2
a
” “
Zio
r p
aTR e agho
” “
A 9E 0 E D C E A 8
C 9DD DD . C9 9 E /9 C 2
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
43. 2/0 P
• w R w
– ifg Ic
• 0 8. A , 4 A. A n Vo” a
• . s , 4 AD N I I tr
– P
– ~v k a S “edcV
l n R V pa
035 3 4 A 3 E 8 8 . AC : 8 34 2/0
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
48. -312 P
• ʼ
– mpdenh xk o
• / 7 m iln V ,3,
urvP AI c
• y _ R w egtl V ba ,7 0
- : xs
– -312K .--3K _ m c R N
C C7 :AA: 7A 8A A 8
7
AC: 7 : : 4
7
49. /,- Pf Ci
• e g
– a o e 384 3 32 3 9 9 RPst
• hO L Z
– vl F r upP w P ZRP d P
o e o FVe no FVe
e 03 93 3 S V
50. 4 b N VP )
•
– P
• 0 A A 7 0 U I7 A i
• 7 2 2l d(tso L agi b
gie U
. , 7 0 A A 7 5A C H
7 4 C A 7 A / 7 A A K 4
a“ b A 7 H
/ 7 f o r p
8 c Ln Oa d
0 A U am Ow
bU ” RiL 7 H r
l d U V Mr l
a d U VL f a
b vul agiL
a OtU l
M
51. - 8& & a Z &
•
– x Z
• u v m ihln d W e y
• rvks v 80, / KR PAFK d
• FNFKJ 4 J “ caopf gtw
5PN FGK B B U B C PLBM FNBA
FBRLKFJ 4B MJFJ /MKI 1I B
-K B FKJNV - 8 & &
/ EP B U FNFKJ 4 J P B F FKJ F E B C
PLBM FNBA PSF F MT 8B NKJFJ : NGNV - 8 & &
2 FBM FK JJF B U. K FJ 4KNNBN CKM
JNPLBM FNBA FABK 8BLMBNBJ FKJ 4B MJFJ V - 8
& &
52. 40 Sf Mip O P
• e g
– n e O o hP
• ya cS s
• b Z
• UV N ldtr R vu yaMXN m
- 0C / E C
2 2 D E : -C C E D
, A C 40 - . E 2 2 D E : DDEC CE
: E E C8 40
5 2 2 D E : 2
/ C 40
53. +5 2% % g L %M
• ap sLlkM
– rioapL gM
– ioThnRV e
• 0 apTN rio uPf ioStm
• 3 +.2L +/. % M
4 + I 3 A ,
8 + C F . 8 5 C
2 A C C +/. % %
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
57. 0 : ey T
• s nmo d h
– PRIC .8 /FD .8cbe
• PRIC .8 S.B 58e
• /FD .8 nmoe T mVle
– kpjc s dc
• 5 251a geeSrteud bi
PRIC .8 0 : 9N I
/COP : MCN 4L LN IC 7C PFL
PPMO DFP AL 8 I O OPRICD
/FD .8 506 9N I
PPMO DFP AL G NLAH /FD .8 :R LNA
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
60. -62 a VN
• w r mhn pas Z
– A9 BD RdaT
• ec A9 BDg P at
• S d2B F G 8F G a“R
• eg Z v a 8D: B A oil e
4 8A F 8 ,D : A F 08C
, F A A9 BD ,8 : 8A: A9 BD
/D . F 9F BA G 8 :8CF G D8 A A
48 C 4 9F BA -62
61. - 85 6
• V R Coe
– V r C - /25 - 6
• - : 9
• : P N 01
H
- C 9 t
62. , 12 d “O ” R S
• snu”Zj
– - dsnu
• - e a e j i
• / C D D F e
3 6 A 8 D -
08 H 5 - D 8 2 D: D
3-. 3 F 1 H , 12
O b3 D : - H D . :R3-.S
Wg j
O mo v mr cV ktl
sp cfh Pc d j
63. a
• po 635 S
– 25 28 2 t ec: r Dn
• /00/ 2 2 N R iC
• 25 83 2 / 2 V ld P
64. 0:6 s v [U ( , V
)
• hie . t ru p n
– 5 1/. / 2 0:6 -
• hie y R2 HI p ] f g
dl R4 IED 2 IED m cS] oR4
2 a hie dr S
3 6 E P9 I D 5 HF D / I DH AH 8 F D
8 F DH C H C 8 EA 8 I D DH 0:6 H E
65. ,62 gmN P
• a r hp ue sd
– E A , hp ue sd
• cbytlw a rin SRVMhp oUR
V2EG :B 8 E a
0 , . A A / A: 4 A BA A - B A 6 : BE
H 5A A .G : : 0B: : C BA ,62
66. /- H P
•
– V O
• R
• 60 , 532 3 02 8 60 328 2 8
• CD H IH /
67. 20 WfnH u
• r dHafs
– MZ hoidX gVle
m d
8 8C 6 E/8C6- 8 0A8 CA6 87
- / 78 A . 6 0 8
, C 6C F 20
d R W P V
t . 6 06A :
5 5 6 : 8C 6 E AA8 6C : ,7:8 0 8
C 06A :F 20
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/
82. ,612 a # R
• 3:8D0 I .D 3: D D I8D D , 8 D
– 3:8D0 I
• ,612 sPport II ##LLL :8D D I # h S l w
– -# - 3 C8DI : /8 1 #.D I8D: 1 3: D
, 8 :8I D
– II ##LLL :8D D I #: 8D :8DD I V
• pn a P P Pu II ## 8 : D I C # :8DD I7 D: C8 #
– V a UV gUe
http://www.scan-net.org/cvpr2020workshop/
sPpab_d N
cyrm _ V fe
-# - okyPm
pn N i np n N
mP piv _ apn h
T
http://www.scan-net.org/cvpr2020workshop/static/img/splash.jpg
83. . 7 g“ W)
(
• 3 D B A IG 2G A M / M W32/
– evVs ig
• :0- f _k nr vVs ig
• e vVs ig e ed
– - LM ,P
• 1 8 M R D D B . ILL 2G B 8 G MD L AI : E
8 ODL 8 G MD 8 BG M MDI S
• g 0.. c a
CMM L BDMC IG 1 I D8 .28 PLLL
https://lidchallenge.github.io/
h g
b gyVw t
i W h b
m i ”mu p W
.I ,MM MDI l e i
s ol
84. . 7 o ”d_
• 4EAR I G FROM LABELE I EOS
– mf bso
• n gu mf
• 2 VITE :PEA ER
– ,L OS A /FROS
– , REA E AL I
– 2VA 4APTEV
– 3ITE RA 5ALI
– 5I G U 4IU
– 7IERRE :ERMA ET
• RALjp atn gu mf
– ,3 7IERGIOVA I ET AL /VOLVI G 4OSSES FOR SUPERVISE I EO
EPRESE TATIO 4EAR I G . 7
https://sites.google.com/view/luv2020
w_ 1- r zy
_0UTURE 7RE .ROSS MO AL RA SLATIO .O TRASTIVE
/MBE I Gml v “n ho j mf
guk cmeiq s v atn
gu v
TTPS RIVE GOOGLE COM FILE LQ8U G E --LJ A.Y S VIEW
85. / 8 k a (
)
• 8PCAM GRGM , :CCG RFPMS F RFC SRSPC
– k qmgnfw rsvtu
• 8PCAM GRGM gl kbg
• x oz ih y o “ihj ep qldngcf
q
•
– OSC PR S CP - 2 GT MD MPM RM
– /FC :S 2MM C C C PAF
– GAI FG CF PR / .CPIC CW
• .C R 8 NCP -U PB
– 0W KGA CSP C RGM 3 DCPC AC DMP MPCA RG P CARMPGC " /M G 2P CP 3 / " - CV BCP
:AFUG 3 /
.C R :RSBC R 8 NCP -U PB
– 5S RG / KCP P CARMPW MPCA RG , 8CBC RPG P CARMPW 8PCBGARGM G CRUMPI MD / KCP " 7 W
:RW C GT MD PUGAI " W 2SF GT MD PUGAI " GARMP : AFC GT MD PUGAI " - CV
MR W CAF M M GA GT
https://sites.google.com/view/ieeecvf-cvpr2020-precognition/home
86. 724 U o nsRP Q
• CF I G 7AHA D G , H A D GI D HA D
– 7t Trk W
• b de eM aM eTrk
• , H A D/ Pb deTi Wumh QM F, H A D
Pb depxQU ecVfg
– v
• 0GAHI D G C DP6DAK H I HIADQ
• KA 2 GAB P G A , /4Q
• GA D / 0 K H B P6DAK 2AIIH G Q
• . A 8 P/ 4 H G Q
https://sites.google.com/view/cvcreative2020
87. -7 4 VR
• -7H G H
– -7 lu n
• -7e lu n c f ag P
– t o 8HA 4 -22 bd
– 74e T l x
– kmi h v
–
• , H 8 G 8G
– B C 8B 8D C B H BB8 D G G 8B C B8N G : D D 8 8BB
B N D DN - 8 G D . B 2 G B 0 8 4 AA /8 8G: 78D
.G D G :A 8D 5 C8H , HB D
– H 8GM G 8 H
rlos p iwyUc kmi
https://vap.aau.dk/cvsports/
88. / Pk Cmz E
•
– e n RPriCgkCV c u
• 7 /7 7 h T s
• [] 7 6 M
• 0 l [] s
– 2A 62 7 2 8E I a
– [] s P o ltK T
https://eyewear-computing.org/EPIC_CVPR20/
89. -9 m R T (
()
• 0 C . M / C C M 8 N M -IG NM 9C CI
– 0 8/ ro S le gm:7
• 8CG CM N u ibt0 8/ m
– / F C C A 0 CFN 2 MCA MCI I 7N C / M MCI C -
• u a m w u bt ih mB MG l
f hn sl dc m u
• VB MG kj u ac u WiV w Sm pm
mr l l ak u Wm k
– , F 0 C MB :CF ,0: . M M
• l t xv m mc m BG E
BMM M ACMBN CI
BMM ACMBN IG C CI DI C P
90. ex U W
• GJ LCMC H FCMR CH GJ M K CLC H
– y gv
• wuarpT d gv
– iVnhmk
– o
– o U uet z
– cb
•
– CH O . JM W H JM 7J H 8CG
– FL -CHHW3 M 2 KHCHA
– FCO W MCOCMR H ,O HM A
– 0CM H K 3 FCEW M H M CH GJ M K CLC H
– HCG L . KAW9HL J KOCL JK L HM MC H
– HAD H S P W/ G HL H D ML CH
ejVlg a eps
http://ai.stanford.edu/~jingweij/cicv/
91. /1 ToqIptN L
• A8 98 ,
– u lRT gc , w
• . / V r S ,nmLT daeb k
• A8 98 0 9E A E 9 : 0 LT hPQ
c P
• 299 7 8 19 EA Sis
E 9 A8 98C A :
https://embodied-ai.org/
92. 0 E cH
• A8 8AC A
– da OSRe
• A8 8AC A L PN
– / A8 8D A8 V T
– A8 8 A 8 08 di b
• mj PN
– A8 8AC A A8A8 29 A 8D A8 T
– / 8 A8 8AC 8A8 V da
– 0 V da
– A 0 ghln o
– da
http://activity-net.org/challenges/2020/index.html
93. 0 km lsMI
• . C 9 C C: 1
– t o R TZa Uc
• 2 C I.: / C 8 .
– duvI nuv y
• . 2 9 : I
– I cW R NTt o
• 3: 9 1 9I C C C C 8 8 C
– Y V Tgf Pe rhi
https://matsui528.github.io/cvpr2020_tutorial_retrieval/
94. / 89 b ) )
• R M H J MT M T H FMMC SH TP,
– i dmwya
• MT M T H FMMC HJJ K 4 2MMFJ
• MT M T H FMMC SH T 9HBI :V JHPIH B AMMI 5M CH 8M RP
2MMFJ K 2Y U M HS PH U M C :BGH CJ 0 ?Y HBG HBG J
2M P J B AMMI
• MT M T H FMMC AR J, - C T H VFHAAM HB MPME
• MT SH TP RP C H G C BHPHM MB PP, H M HM H
2MMFJ 2 F M H M JHP -4 :HKM PH HS PH U
https://www.youtube.com/watch?v=W1zPtTt43LI
MR RA if hvnspur zatou gbm
g k mlb k( maZ
ecg
96. yVe ,623 R
D G 0 GD D 2G 9 9B CC IG
- GC 9B - 19A I G C /C D I B
+ I 2 GR
– p D B /C RUd -
• - I B9 s /BB C D I D wg
• hmjo t
p Udr -
cmi lnO
w g
R2 I C IG I D D gyS -
P3 + Ud g , D D C
6 L DI - I B9 0 IRO
6 L - I 0 I B9 g f u c w
g W sv3 +g P, D D C b
a 3 D IG I D 0 Rg P
97. n P RNV- 23 M
)(
• , 2 9D /9 9B D : - A 7D 09C 9C , B
A 79 2 BD D : ,9CD D 9 D 2 A9B
, B A 79 2 BD D :ao S vr y lm S
, 2 9Dau
, 2 9D s y lm BAV tp eh i
dcgV alm w : 9 9G 397 CDB 7D A9 D
9 7 :V , 2 9D w
98. plU VR 2 P
)(
• - /DCD9 B C D B C9
H C 3 DC H 3H : CH
DCD 8 / CH DC
– / H B DCS M: C
B C D k 9 tW -00
vsN
– DC o W u nwW M
DD 9 DH i N
m cbdeg fh ar
99. m a 34 ”O P
))
• 0 O I7 A P
– 0 A A 7
– p Nb L0 A U ml bo sN
• b 7 H t n
• b c wn
. , 7 0 A A 7 A C H
7 4 C A 7 A / 7 A A K 34
c A 7 H / 7
t r l d RL
U l ceo sN lcU
9
7 H t n fl U Vl
Mt n b fl U VL
b c wn l
bi L b NvU
l n
7 2 2n f(vu
L bi “c i g R
U
100. nb g/ (
• / 5 M -P 5 GDH
– H DMD H -1 o c D D MD 2 0 D H G am o T
– M HM o G DHB S3NG Hi y u i H M DHM o tam f
hjlS2 y u h y u nd g w D H G i o
h cT
4N MD D HM E M am R
hjl4N MD T
sU) M D 5 Mf M D 5 Me
DG H D H 2 N D H M HM D o mV 0 MC/ MD H5 Me
G HMD BND e v o pr V D H 5 Me
D H A MN G o V D MD H5 Me0 M GDHD MD f M C MD
D H G o
101. lg 12 t K) (L
• C ACC A A A
A 3 / 2 A D A
0A
– 6A 3 / 2 A D A 63/2 W a C
CSC A CWo V v wV -CA D C A CT
C C CW fe A sr i wSN VPRI
– sign words SOTA 63/2W w u
WcnV I
-CA D 3 6A CT3 0 CdW A
sr i wI
v w mgPR63/2 a
102. i - 3 ) (R
• -EC ,7 1 ACE CE /A B 0 7EB B
7E 2CA B
– D 1 E 0 7EB B 10 b B 7 7AD b
a Vh-EC 7 A ACE ,1 N
– ,1aegMtxoA B 7 d c Pnmpr f
7E B 7 D7 E b a VM ,1
b 10 a h N
Slow drift phenomena
Embedding features
drift
Hard negative examples
,1v x 37 E 7 yl L7B CEX a vwPs
103. l Vc 3 S) (T
(
• G 0 F CB 8 2CA B
– 8 G A 1- / 8BB B e geD C FF cG G F CB m
B FG8B B G CBdG G G Fm A G G G C
m 8 G A a b G e m gil
– 1- / F 8BB B tyRv Rod d a NFD b
m il
tyRv Roe lP 1 CA G D 8G afrup bnx
R m nwxsxwR b e m V G
D 8G af l b G e m gN
D FD G 8DD B d e- B B m V
104. wVd T , 34 R (S
)
• E 9 (- D 9D D 9 1EC D
– /E D D J Ub EEC 9 E (- E A EL
E A C e r D E D mjln ac sO
– (- ihjl ( ig 2 ue xO
/E D D D 9D D E A ED EDeoT
ye O
D J Ub r 09 E N2 A
EL C e s
t P sv x
105. wrRb )0 I
• 0 1 ) 1 C (A1 55
– O TaN NIds
– ) 1 C O x mcTlO P t
– u d O j d
o ) 1 C d V a /5 I h i 2 53
I e 2 53 5 13 IdnV) 1 CN Ndy
106. b R 0: 9 % % O (P
%
• 2 C 1
– 2 C 6
• 2 C 6 3054 % - E . I D @ -% -)
– rxtM o Mpesc a
• n c
• 2 C 1 1 ( /0707c
https://arxiv.org/pdf/1911.09070.pdf
h o o MpesL
c a
O P c
V h gi
Mf g vc O
P n O1%N1 P
rxtM a n
107. TK N/745 ifD E
• 8 0
– 1 9 : 3: P p
– XCV n e
• P XCVe R o c
– 2 : 9@ - .6 , .6
http://openaccess.thecvf.com/content_CVPR_2020/papers/Feichtenhofer_X3D_Expanding_Architectures_for
_Efficient_Video_Recognition_CVPR_2020_paper.pdf
108. TgWM 12
• 8 D 1 C D
– iro tn a h V
– 1 C F D A E D Ec
• d sIo
– a iro pl NL c30
a I Il 2 / Ea
rlzI Oe Il
3 -ADD 2 A
1CA AD 20 1AA Oea
h L iro
s C E -ADD h M
H Il a 20
1AA c V H
P Tg tn
H L m dM ca
R c I h S
H
109. g R - 4 P
• /G HF2 , BB
– b oOmlnp
– h Sf “h a
0 . B /G HF2 , BB
1 G : B , : C GA G / G B 39 :
/G HF - 4
L “h aoOmlnp c
eL npr cfsni kh t
fN
oOmlnp V j
FH G HF
110. S - 34 P
• , F A 0:A :
– p n k
• k”
– - 7D7 : .7F7 :Fk a
• J m o J 2 l s J 2
1 1 :F 7 , F A 0:A : , F A 7
- C F A 5C7F F: C D7 5 :A:
0D7C - 34
a f k
a” srtw h R s
R Vi c/:G F
a R e“ Ri
c s dh R RS
e i J a S M
https://www.actiongenome.org/
111. f , 5 V W
• 1.M0-
– hsv ce dx - g
• tmwq j rikw b z g e
• 2G 0A 5 GDMLAGFa “ou vwpln
– ,GD : mt
• LLH GD : GG D G A :D 6 G C.IC 3 / 8 L
6 6 ALG L D R 1.M0- 3MDLA 2 D AP D
DA F 1 HDA AL .MF LAGF G 0A
5 GDMLAGF - 0M F -A ALA LAGFS , 5
LLH MF MC ALG AL M: AG 1.M0-
112. r e 3/0 N
• C - B G 7 AC D
7 AC C 7 A
– pxlaSP g h b c s ib
/AD . b Tt d f
• /AD . bm V w aSVdg h v
A n o f RZ “f
1 C - B
G 7 AC D 7 AC
,C 7 A F B F 2C
1 B 1 7 A 3/0
113. Zj a 3/0 R S
• CA A B . 8 F
– ” c ec p
• ic da k“
• C B B C k ” Ti
1 CI A CA A B
. 8 F B A 0 B B
C 1 , 1 D / C F 3/0
a1 B F B8 , A R1 ,S
Wg ” k ”
“ bVT n
bfh Pb “ c k
v sc 20 Wgc
c cuxlo
vrs t bah iO
114. ic
• 49 1 3/ 13 1 2 / 4031 143
– e gt r w nl
• R Pu TV d C o
Ps
• aF r w C
115. D F ea
• 2 310 51/ 1 1/
– lf ikjg lf
– / C PR cd V O b
116. S FN 0- D E
• 1 / / 6 1
– it sc C m g
– e V C fL g R
– 2 2 lP noP r / / 3
1 6P d ap
117. • 0 1 3 7 2
– bT A 0 1 0202/
– dR P dR a
dRCe
g V
118. n P S 0 us
• / 3 ?8 -38 -? 3 32 3 1
0 ?18 1
– xl Uwr f gt a o iva
U iv eag Ti k
– fd oUpIeagTi V wr H C
Ti RU 3 3 wr H
120. M CDB
• L M
– ,0 /0 8 , 0 / 1 28 /3,
– VN P
2020
1960
CNN
Perceptron
Neocognitron
1980
1990
2000
2010
1970
HOG/SIFT
BoF/SVM
1st AI 2nd AI 3rd AI
Deep
Learning
F. Rosenblatt et al. “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms” in 1961.
Rumelhart et al. “Learning representations by back-propagating errors” in Nature 1986.
K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, in
1980
Y. LeCun et al. “Gradient-based learning applied to document recognition” in IEEE 1998.
Backprop.
121. .66 y - i s
• AL AKNL I 6A CIENL I - IP6AN
– AL AKNL I
• tv fkegos zwu ab
– GNE G SAL AL AKNL I
• AL AKNL I w
– 6A CIENL I - IP6AN
• “d t mehp u c x
AL AKNL I lYhnirs
6A CIENL I
3 0 F MDE U6A CIENL I 9AGB LC IE EIC 6A L G 6AN LF
AG B L A D IEM B NNALI 8A CIENE I :I BBA NA S 9DEBN
EI MENE I V ,E G -S AIANE M KK
DNNKM L NI LC L I K GE K KALM 0 F MDE K B
- IP G NE I G 6A L G 6AN
A- I AN G U1L EAIN , MA A LIEIC
KKGEA N . AIN 8A CIENE I V 2///
DNNK S II GA I AR K GEM K B G
A I K B
122. 1 0 kp l R
%
• 4 9 80d U ihf x
– .E N - g 4 9 80 %
• % s r Ywt u r Pn494R
hoela B , EC B I G BC D : BCDL E I C A
– / EC @ 7IG A CG 8 : 931 1IG GL
d1227 U S V
123. -- . N !
--C D
– /31-1 IC V
– 8 73
4 !! /31 71 8:3!
4 !!0 : 410 2 08 !
×
124. -11 , in I
0 8 1 E V
– D 98E C D
– gc F + , 23t
– R g PVF r
http://fungai.org/images/blog/imagenet-logo.png
https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_t
o_understand_pictures/up-next?language=ja
0 8 1 E oeF 4E8 C F
2C 9 E FRPVr
7 C 2 89 c I P
/ / 5 - I s
F dmelf r W
gc aN TS
125. ot 0 a
r /
– 62 P 0 N AD
– 0 lV T U G
PC A DI
– s tdI e
https://www.nextplatform.com/2015/03/18/nvidia-tweaks-pascal-gpus-for-deep-learning-push/
3 1 P05 81
s td
126. 177e 0 epuv , (
+
• e z )V +
– )w mik l age x] [
– d e 9 KC 7 L H D
/ P7 L 5 CR NKD 46 90
227 L C HG G 46 90 )
2HHA6 7 L R A 46 90 ) 0 89
9 K7 L 3 46 90 0 89 +
46 90 CGG S16e
+ rnpS I sote
ILSVRC2014 winner 22
46 90 CGG S y ( h
127. 3:: lnZ2 ehif - (-
,
• p tukZ lk
– R: Sms adbcg
• R: S 3 NR : S 4: S
– lkZrok : T BL 0 DI S DST B DI 4GG D NS: S
• :0 : S 4GG D NS: S
4: S 4 CL D
J. Hu et al. “Squeeze-and-Excitation Networks, N 097 .
ISSPR/ B H BCR , . ,
S BL 0HH HBS E R ETBL
BNRG MBS NR G 3 P : T BL
: SV R N 2 ,
ISSPR/ B H BCR , . ,
R: S
5 6TBNH S BL 3 NR LX 2 NN DS E
2 N LTS NBL : SV R N 2
,
ISSPR/ B H BCR - ..(
3 NR : S 1 A PI S BL 8 B N NH BNRG BCL
0 DI S DST R G DBLBCL 7MBH
D HN S N N 2 -
ISSPR/ B H BCR , , ,
:0 : S
2 8 T S BL H RR : T BL
0 DI S DST B DI N 2 -
ISSPR/ B H BCR , .
:0 : S
9 BN S BL 4GG D NS: S/ SI N NH
9 E L DBL NH G 2 N LTS NBL : T BL
: SV R N 7298 .
ISSPR/ B H PEG . .) PEG
4GG D NS: S
128. 1 gU ] V-&),
(,
• s [ Y neVmt rU m r
– idha. : 2 OP&2 OP N : 5 1
– l. 2 F P P
– O L 5 LFQ F . mtock 9/ OQ 1 F
– m r. S OPN LR PO )1 LR ( 1 LR
Person
Uma
G S LD LT O 8: +A
: 3 NOG CI 8: A
2 5 LF 8: +A
S PN LT L 48 A
129. 5 o a4Cox yd( /e
()0
Hito
Uma
9HH SPRL FCPVSH 4C @ (G
2 H3VVXY
7HXY @ 4 F8P XOP R 44C(,G
@ VVSP N SYP YHXR VXX 7HXYL @ 4 F@L A(,G
@
@ 4 dju 9H HMYL I L Y LXXe f ”
L XOVY 5LYL YV nr nrd” v klle
E [( [) [ F@L TV 4C @(- 4C @(. H DP[(/G
L XOVY LYL YV M SS V L Y SH L
HYLXY 2SNV PYOT ”
HXR @ 4 F9L 44C(.G
@V 2SPN 5LY ALN
IIV] XLNTL YHYPV o i
n n tms HXR @ 4 v jh
+( /2 1 A4 4
IIV]o i n
tms@LYP H LYv
gtoi wb
+ /2 1 A4 4
AA5 F P 644C(-G
L XOVY LYL YV 2 OV 3V]
9H HMYL MLHY L o
9 8 F5HSHS 4C @ ,G
AC
47 FDollár 3 C4 0G
AVMY HX H L
5 FFelzenszwalb
B 2 ()G
HYL Y AC
• o ud) (c) (.e
@ 4 F8P XOP R 4C @(+G
ALSL YP[L ALH O 4
130. 3 h 2Bhrvwsa ,b
h la , b
1 MRKL I EP 0R 0REP VMV S EPI 7R E MER I MR
FNI 3I I MSR MR 2B ,
L TV. E M S K EFV + , ,-
7
1 MRKL I EP 7 4 . 4 M MIR :XP M EPI
A EMRMRK MR IX 7 ,
L TV. TETI V RMTV TETI , VRMTI I M MIR
QXP M V EPI EMRMRK TH
7 4
> DLES I EP : 3I . 0 MRKPI LS FNI
3I I S FEVIH SR :XP M 9I IP 5IE X I EQMH
I ZS O MR 0007 -
L TV. E M S K EFV ,
: 3I
D CMRK M I EP FNI V EV SMR V MR E CM -
L TV. E M S K EFV - +,
2IR I I
9EZ I EP 2S RI I . 3I I MRK FNI V EV
EM IH 8I TSMR V MR 422B ,
L TV. E M S K EFV , ,
2S RI I
8 3XER I EP 2IR I I . 8I TSMR A MTPI V S
FNI 3I I MSR MR 722B -
L TV. E M S K EFV - , ,-
2IR I I
:EVO 2 + / 2IR I I + ig
d e “fk m x h u t
a bm y cj onpm y onpm
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
132. /22 tvO. efdP Q
– . 214P y sQ xi o
• r P0 8 : .8C A A Q
• n up P 8 5 : A A I:D A , 5 Q
• 8 / 8
8 u 5 l N
p
l V
g
R 5
R0 8 : .8C A A R 8 / 8
a m mw V
p h
https://visualqa.org/
3 A 8 : 8 LShow and Tell: A
Neural Image Caption Generator A
. 4 )
C 8D D C ; ( ())) C ;
C 8 8 D
133. /33 N.8 mv nP )+
))
• sluP3 / C DC .DC
– (/, D .DC 3 Pow
– (/ slu, .33 1 2P r g R cb
– )/, )/ .33Pl iS Tb VbS
– ( /, :A .DC P“ l i fp t
)/ a aP.)/ )/ 3 0)/
)/oeui
a arkp
P)/
/ C A L- .AD 1DD D D A
.DC DAI DC D - DC D C DC M C .84 ( +
134. 188 n T0 ab )-V
)
• nvr
5MGN C S1C NC 0 L GK G E 2QC N G
G CKN S G 400 (& ,
https://arxiv.org/abs/1705.00754
1C NC0 L GK G E
G C S G E C K CILKM /A GK
1C CA GK S G /07 77 (& ,
https://arxiv.org/abs/1710.06236
/1
GP C S C M G E L GK CILKM CLMCNC GK
RG KA 3 K 1GDDPNGK S G 0 9 (& .
https://arxiv.org/abs/1906.05571
KA 3 K 1GDDPNGK 31
nv ls d
nv ui en tpTwpV
oTc gh d f m nvr V
135. V N 1
• 8 6:
– / 3D
– /
GAN
https://medium.com/@sunnerli/the-
missing-piece-of-gan-d091604a615a
/
BigGAN https://arxiv.org/pdf/1809.11096.pdf
1 A C 5G8
136. 288i [1 ix y # ,]
• 3/8i g l
3/8 p u i3/8]
• A3 GNN X 84: B R .##RCRGS PKR #RCRGS# IGPGSC KWG C WGS CSKCN PG R
213/8 j j i ]
• A C S 416 B R .##CS KW SI#CD #
:K :K RK GN af e1 P K K PCNg3/8]
• A4 NC 1 : B R .##CS KW SI#CD #
1Z NG3/8 RK RK i gb ]
• A V 411 B R .##CS KW SI#R # - R
/13/8 vs k h bdr wot fbc]
• A9 GSC 4176 B R .##CS KW SI#CD # - ,
3/8# 83/8 ]
• A/SL W MZ 4176 B R.##RS GG KPI ONS RSG #W #CSL W MZ C ON
• A7KZC 416 ,B R .##CS KW SI#CD # , -
:33/8 ]
• A5CSSC 416 ,B R .##CS KW SI#CD # -
, GN / GP K P 3/8 nv t m ]
• A CPI CS KW , , ,B R .##CS KW SI#CD # , , ,
- 0KI3/8 3/8]
• A0S M 416 -B R .##CS KW SI#CD # , - -
, ei
137. 377c yM2:c opl , ,S
• sa x 507 G :04
– ioha xd507 Q - S
• 97507 1B 507 9 C 507
– :8 :04 :04 g fV
• 507ce 6D 2DCC EG g a
x trc y v
N:8 :04 N1B 507
0 Razavi et al. “Generating Diverse High-
Fidelity Imageswith VQ-VAE-2,” NeurIPS,
2019.
A EG. B D E - E
138. -- 3 lsum
• Re a Ve
– iOtOm
• A 9 9 9 C9D A
• 9D A9 9I F 9 DA A
• 1D A 9D ,9 DA A
• A 8 CF F A
• /9 A D 9 9AF ,9 DA A
– V S { g UfLdT Zf
c }
– v 1 dT v pnrg W c g
f
• hr ko w a N
139. ))V ,Vmx n C
• V
– / -/ 2 /30 /8
• /8 C
pk u { l i
• / /8 C
pk u l iS l i
• -/ 2 /8 C
S V l i
– / U vs VW
• /30 /8 } C
} D}
– if V U} NV
U if V Vr a o
– / e
140. //Z ,4ZhnoiN
N3 8B : .:8 B B Z t
– 38 CBCA 6 8A ,401
• ,401 : 08D: 8
• Ze asZuy
– ,4Z Z L w T P e a S
– p V Zu ]
D 8 CBCA 8B C :
g edfh[ e a r lkm R
38 2 A 8 3 :: V e asZu
141. 0 l V/:l ʼ y
• [p P
– z i [p k g
• 3 B D HR 2 , hj] d 2 kn w v l r c
• 0 6 EA?N 0ANA?ND e l i 1//: .A N AL
• /ON NA 4A L o l mi d
9O ALHARAL 1//: 6L G .
z b/2w vi 0
aok pr bd
8AHAS 1//: 6L G
/ON NAi ut z r
pk c
9CHAG P 1//:
2 l lw
v s
142. 33 /8 kstl ()&)
(
S I 2 9 D D
– a uVL a
• v ifb 9 . L ifb 9 9 .
• N r V1C9 3 9 rS 1C9 3
9 Nec Pmgr
9 a,x dp nrV T
( 9 ;9 84/ 1C9 3
O1C9 3 v x
143. .33 rv efaM +N
gk m ns
– 0 3 i u
• 5 14 5 /C C : C C D 1 C : DF 4 C D
• l to https://arxiv.org/abs/2002.05709
5 14 d L uSwR
M5F C D 8 p 2 D 5 14 p %%2NSI
i
V m
144. loD G
• dm
– a
• /A 4 2 1 1
• 3 4 1 18 41 5
• 1 1 5 4 1
• 1 .1 3 1
– Ncm /A 4 2G sm
– eg 3 4 1 18 41 5G
• vz Su dm
• cm V rL
– y 1 1 5 4 1 G
• SR i 4 5 t Ch C cG n
145. 11P A 5P i D
C i SP
– / 1 R p n G R
• B4 / 1
– 523 7 26 7
– /Ve f t I 378 7 D3 N D
• B/ 6 / 1
– 0 3
– s Vr N C Ve f t I a
C C iVm
146. 188n V0 n ,
%
• /DS KC :DPF KI 5 ED8DP
a sk
BN PBF MND PN Kn
m
n jo5 ED8DP ND
PN K a i]sTTT
7 F H K 200 %,
3/o 8 n4 FP Ej c n / 1/
[
M %- , R :D 8D P % %
o 8 n
https://venturebeat.com/2018/05/02/facebook-is-using-instagram-photos-and-hashtags-to-improve-its-computer-vision/
5 ED8DPo n wvn m ds
• c ]
• wv yj a dtp n j
– gc % 6 n o
• o a h nj Xw m
n j ken
rttp j s
[ 4D N %,
wvm c ds r n
147. /44 y M 8 nrsoP + +
• y /- t
– y g n e V
– y
2C IC 2 K G9C
3 D I C 7CD 3 GI G9C +
0B CK G D 856 ,
2C IC 3 D I C 7CD
c f y ,c f y
,c f y
1 I G D 3
R v y mN i n
a T y mN i n
[ dhnnrso X V]
148. // 2 iorjM# N
• // koL Lf K nnLg
– 3 3 # 19 3 93 1 , 3
1 9 1 9 3C D/ C 3 8 /1,
F/ C 3 38
M y N
– 3 T b 2 Vc // x
https://chainer.org/images/logo.png
http://pytorch.org/docs/master/_static/pytorch-logo-dark.svg
https://www.tensorflow.org/_static/image
s/tensorflow/logo.png
st 93 1 v 1 9 xv 3 # 1 b
, 3 a e c
3 4 1 9 lphfi 3 # aP wV
149. 4 dv 3Adlp m ( (-a
).
• 8 3/ no kh icfg dz
– 1B> 1 WTI 7RR I 3 RWH rtbe
UWFEPI( 1239 6W ENW GRPM URR
• :W VM 7 :W VM RHI
– 9PE I IV d
• . 0 0 ( P 0 P 0 P 0 -P 0 P 0 P
u s > 21:5 ( x 1239
https://commons.wikimedia.org/wiki/
File:TSUBAME_3.0_PA075096.jpg
: DEPE ENM IV E DIV 1 RV IT 1GGI ITEVIH >74/ IU IV
TEM M R 9PE I IV M ,) , UIGR HU ETCMX STI STM V
. ( .
VVSU/ ETYMX RT SH . ( SH
151. f V-:fsyzt & &
• , ,8 8A M C - MN , ,8
– f dc ol g e
– g21 1 O NAOe bf ] i
– g f ujvon zf
21 1 ,POK KIKPN M C A DI MG
oryk pmwzr
n z h5 O / KR 8OAMAK 3 O D C 5 FA O AOA O K
7K 5 KIAOMS 8AI O 8ACIA O O K
0A CAM -: 7
2 I 1--:
f f
ec h a
e [n zh
8PTPG 2 O KG
-: 7
152. 33 y T,9 irtjW
• smhacf
– _ E N N ND I
– nklorVept I /O D I N
W “
– uy i gbtd W “
, : I N G R I /O D I+ E N 5
. ND ND I 1N L ND P I /O D I S DI
,956
CNN + I NC P IN IN ,956 L : I
I /O D I E N 5 . ND ND I 1N L NDP I /O D I
,956 L
0D I G N G R2 P L DI C , G ND I L D
8L FDI S DI ,956
http://openaccess.thecvf.com/content_CVPR_2019/papers/Giancola_Leveraging_Shape_Completion_for_3D_Sia
mese_Tracking_CVPR_2019_paper.pdf
i gbtd Wv
153. /44 P.8 grshR S
• l danst
– l dans e ans
– / 1 ABGF R S
• / 1 ABGF v Y om
– 1./- R S
• 1./- v k ip VY f c_rs s
9 2 D / 1 ABGF , 8 I BD - F AE IC GI
/ BGF 5G 0 BE BGF E F BGF F
3 F B B BGF G .DG ABF 3E N BF .85
A G F A GE GF F :.85 : I 2 :/ 1 ABGF :,:
8 I BD :- F AE IC: GI:/ BGF:5G :0 BE BGF: E F BGF: F :.85 :
: I
H. Kataoka, K. Abe, M. Minoguchi, A. Nakamura, Y. Satoh, "Ten-
million-order Human Database for World-wide Fashion Culture
Analysis", in CVPR 2019 Workshop on FFSS-USAD.
A G F A GE GF F :.85 : I 2 :/ 1 ABGF :,:8 I BD :-
F AE IC: GI:/ BGF:5G :0 BE BGF: E F BGF: F :.85 : : I
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
155. 055 L/ nu oN
&
• / nu o, gtis r
– ea aS S
• l h T e
– pmn
•
y DC 3 C # E FD : 3.
/D E DC A 6 D D F E .0. mn
– pmn
• 0 0 C C A C . D 3
2.81
– pmn V M
•
f X XP
156. 22d T-8dw} V W
• # # -8 dw}
– vUsd {y n Zif
• d N pU urtz k S
• F N evUs vUs
– vUs d U U
• 7F C 5 E 5 D R 5L 1 F F
• : 3F 0 N R A D F F
• E F , F E F F E F 5OF A
, D 1 F F ,L E F F 2,5 / F
6 F F
h lk
hamoS chg g g
157. 22c U,8cmstnW
,8 5 w d w e
– ,8d lVkv Zc u f“
• ,8 5 c L d b hy
C I F R 0.N/ S
,8 5
B L LBNHLN L C I AC BN
CI 0.N/
N F R7HLN OCL 1 HCHA I
I F C I F
3 D L I 0 A L CH B CF S ,8 5
W L P
: B IO F
R N IF FCHA
3 D L S ,8 5
ba
itp Vkojrtd
c h gw e
159. 6 5 7 6 5 7 97 P
-
C
– 3 ;7 a P o
• /412 E . l.
• /412 ,E-+- . l.
• 0//4 ,E++ . l.
• /412 -E - . - l.
• /412 E + . l.
– pv i g rnh
• ;8 crn TV 7 t P R
P R
lE e
lE +f
160. + +. 00
– i m e p
– h t r al e
/ 6 b
G e ug
alH e / 6 b
. 10vn
e i c
https://github.com/cvpaperchallenge/ECCV201
8_Survey/blob/master/ECCV2018_Survey.md
http://xpaperchallenge.org/cv/survey
/cvpr2019_summaries/
161. 52 2 . 00 1-
6
– @
•
•
– n
• j e cg
– 7 j
• v
– t v 7 p /
– t l 7
ag
r
h h •h h h h h h
162. 1 < 61
• x = {cv, nl, robot}
– x N RN LV h
P eg
1 < 61 + . /:2:> ab c Ni C
robotpaper.challenge
163. T V SK N
P R K
/ 2 0 / 2 2 : 0 2 0 . / 2
20 / , / : / C : / / / /
164.
165. _g N
• . .;7 N . F 7 N
– Zl lb wy
– . .;7 N
• b R Wi _ Y
• R Ul ZP R
• t r b tm a S
– . F 7 p o N
• a T h S R _W
– a c 0 ;v a 1
– FFC F ; 9 9 7 ; 1 977 6 / :;
• R Vl tu b nt ws cWi
– nt ws b5 a Rde
166. S
• .2 5 .2
– Pe
– .2 u
• u r
• b T Y v Y
– .2
• v P o
– v r W
• lab l i Y S ;
– s R / V .2P V
– tW Y b Pe b i CY W ;
167. O &
• /-. .1 3 5 /-. / 3
– .1 3 / 3
• / v C e
• lae 9 t iu I V e
P RC
• V e s t iu D
r e OC