-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathcrossminds1.out
5973 lines (5574 loc) · 183 KB
/
crossminds1.out
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
nohup: ignoring input
['CVPR 2020', 'CoRL 2020', 'EMNLP 2020', 'NeurIPS 2020', 'ACL 2020', 'BMVC 2020', 'CIKM 2020', 'CVPR 2019', 'ECAI 2020', 'ECCV 2020', 'GOTO Conference', 'GTC 2020', 'Hot Chips 2020', 'ICALP 2020', 'ICCV 2019', 'ICML 2020', 'ICRA 2020', 'MICCAI 2020', 'NeurIPS 2019', 'PODC 2020', 'POPL 2020', 'RecSys 2020', 'SAIL 2020', 'SIGGRAPH 2020', 'SIGIR 2020', 'SPAA 2020', 'STOC 2020', 'WACV 2021']
0%| | 0/28 [00:00<?, ?it/s]
4%|▎ | 1/28 [00:16<07:27, 16.58s/it]
7%|▋ | 2/28 [00:26<06:20, 14.64s/it]
11%|█ | 3/28 [00:28<04:33, 10.93s/it]
14%|█▍ | 4/28 [00:35<03:47, 9.49s/it]
18%|█▊ | 5/28 [00:37<02:47, 7.27s/it]
21%|██▏ | 6/28 [00:40<02:14, 6.14s/it]
25%|██▌ | 7/28 [00:42<01:43, 4.92s/it]
29%|██▊ | 8/28 [00:46<01:29, 4.48s/it]
32%|███▏ | 9/28 [00:47<01:05, 3.44s/it]
36%|███▌ | 10/28 [00:53<01:18, 4.36s/it]
39%|███▉ | 11/28 [00:56<01:03, 3.74s/it]
43%|████▎ | 12/28 [00:57<00:47, 2.97s/it]
46%|████▋ | 13/28 [00:58<00:35, 2.39s/it]
50%|█████ | 14/28 [01:09<01:12, 5.19s/it]
54%|█████▎ | 15/28 [01:11<00:52, 4.03s/it]
57%|█████▋ | 16/28 [01:13<00:41, 3.48s/it]
61%|██████ | 17/28 [01:17<00:38, 3.50s/it]
64%|██████▍ | 18/28 [01:21<00:36, 3.67s/it]
68%|██████▊ | 19/28 [01:23<00:29, 3.25s/it]
71%|███████▏ | 20/28 [01:25<00:24, 3.03s/it]
75%|███████▌ | 21/28 [01:29<00:22, 3.21s/it]
79%|███████▊ | 22/28 [01:33<00:20, 3.34s/it]
82%|████████▏ | 23/28 [01:34<00:13, 2.63s/it]
86%|████████▌ | 24/28 [01:36<00:10, 2.53s/it]
89%|████████▉ | 25/28 [01:37<00:06, 2.12s/it]
93%|█████████▎| 26/28 [01:42<00:05, 2.90s/it]
96%|█████████▋| 27/28 [01:49<00:04, 4.19s/it]
100%|██████████| 28/28 [01:50<00:00, 3.21s/it]
100%|██████████| 28/28 [01:50<00:00, 3.95s/it]
0%| | 0/3374 [00:00<?, ?it/s]id: 5f6bf4031dc9eff099d892c7
title: [CVPR 2020 Award Nominee] Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacti...
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://www.youtube.com/embed/6FFBJVV4dAA
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 1/3374 [00:02<2:08:16, 2.28s/it]id: 5f6bf6411dc9eff099d892c9
title: [CVPR 2020 Award Nominee] Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Sele...
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.02424.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/CnchEvVhI3c
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/sfzhang15/ATSS
save successfully!
0%| | 2/3374 [00:04<2:07:56, 2.28s/it]id: 5f6bf65b1dc9eff099d892cb
title: [CVPR 2020 Award Nominee] Momentum Contrast for Unsupervised Visual Representation Learning
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.05722.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/4VVGtYPM8JE
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 3/3374 [00:06<2:04:23, 2.21s/it]id: 5f6bfbad1dc9eff099d892cd
title: [CVPR 2020 Award Nominee] Disentangled Image Generation Through Structured Noise Injection
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.12411.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/vM8kpVr6UQY
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 4/3374 [00:08<2:04:59, 2.23s/it]id: 5f6bfbc41dc9eff099d892cf
title: [CVPR 2020 Award Nominee] UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.05763.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/HRaxIwDfZSk
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 5/3374 [00:10<2:02:36, 2.18s/it]id: 5f6bfc041dc9eff099d892d1
title: [CVPR 2020 Award Nominee] TextureFusion: High-Quality Texture Acquisition for Real-Time RGB-D Scanning
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://www.youtube.com/embed/VnqaqkSgAQg
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 6/3374 [00:12<1:58:37, 2.11s/it]id: 5f6bfc191dc9eff099d892d3
title: [CVPR 2020 Award Nominee] Controllable Orthogonalization in Training DNNs
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.00917.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/mtmgzGsZt14
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 7/3374 [00:14<1:57:02, 2.09s/it]id: 5f6bfc2f1dc9eff099d892d5
title: [CVPR 2020 Award Nominee] Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a...
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2002.12212.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/jUIGpWFybJs
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 8/3374 [00:17<1:56:59, 2.09s/it]id: 5f6bfc411dc9eff099d892d7
title: [CVPR 2020 Award Nominee] Transferring Cross-Domain Knowledge for Video Sign Language Recognition
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2003.03703.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/gd5cLb2re-0
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 9/3374 [00:19<1:59:35, 2.13s/it]id: 5f6bfc6b1dc9eff099d892db
title: [CVPR 2020 Award Nominee] CvxNet: Learnable Convex Decomposition
authors: ComputerVisionFoundation Videos
abstract: away from the topology of the geometry they need to represent. However, at testing time, convexes can also generate explicit representations - polygonal meshes - which can then be used in any downstream application. We introduce a network architecture to represent a low dimensional family of convexes. This family is automatically derived via an auto-encoding process. We investigate the applications of this architecture including automatic convex decomposition, image to 3D reconstruction, and part-based shape retrieval.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1909.05736.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/dfWWtwE8PEo
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 10/3374 [00:21<1:55:09, 2.05s/it]id: 5f6bfc981dc9eff099d892dd
title: [CVPR 2020 Award Nominee] The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.07135.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/_g-oXYMhz4M
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 11/3374 [00:23<1:53:20, 2.02s/it]id: 5f6bfcbf1dc9eff099d892df
title: [CVPR 2020 Award Nominee] High-Performance Long-Term Tracking With Meta-Updater
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.00305.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/0wgL79ZvXi4
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/Daikenan/LTMU
save successfully!
0%| | 12/3374 [00:25<1:52:26, 2.01s/it]id: 5f6bfcd91dc9eff099d892e1
title: [CVPR 2020 Award Nominee] Deep Iterative Surface Normal Estimation
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1904.07172.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/DtlK_h-7Ja8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 13/3374 [00:27<1:52:57, 2.02s/it]id: 5f6bfced1dc9eff099d892e3
title: [CVPR 2020 Award Nominee] Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2005.14310.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/Oa2GKJh4zbA
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 14/3374 [00:29<1:55:53, 2.07s/it]id: 5f6bfd011dc9eff099d892e5
title: [CVPR 2020 Award Nominee] Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.04573.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/t2qpBg9neUc
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://gberta.github.io/maskprop/
save successfully!
0%| | 15/3374 [00:31<1:54:06, 2.04s/it]id: 5f6bfd211dc9eff099d892e7
title: [CVPR 2020 Award Nominee] Robust Learning Through Cross-Task Consistency
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2006.04096.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/XOUf4rN4kUg
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
0%| | 16/3374 [00:33<1:57:12, 2.09s/it]id: 5f6bfd351dc9eff099d892e9
title: [CVPR 2020 Award Nominee] Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2003.14286.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/_K15Gg7MNTY
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/LIX-shape-analysis/GeomFmaps
save successfully!
1%| | 17/3374 [00:35<1:55:16, 2.06s/it]id: 5f6bfd4a1dc9eff099d892eb
title: [CVPR 2020 Award Nominee] Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.01946.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/Z-CnbBBBQHU
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 18/3374 [00:37<1:55:06, 2.06s/it]id: 5f6bfd5a1dc9eff099d892ed
title: [CVPR 2020 Award Nominee] Cross-Batch Memory for Embedding Learning
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.06798.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/SDKDSvv9oTk
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 19/3374 [00:39<1:53:49, 2.04s/it]id: 5f6bfd7f1dc9eff099d892ef
title: [CVPR 2020 Award Nominee] Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
authors: ComputerVisionFoundation Videos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.00157.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/r0CjykCZnlk
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 20/3374 [00:41<1:53:57, 2.04s/it]id: 5ee5bf7eb1267e24b0ec215f
title: [CVPR 2020 Award Nominee] Optimizing Rank-based Metrics with Blackbox Differentiation - 5 minute oral
authors: Georg Martius
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.03500.pdf
pdfPath:
videoUrl: https://www.youtube.com/embed/UtOG3utfd5s
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 21/3374 [00:43<1:53:06, 2.02s/it]id: 5fc9b27532b2bd365cb4a37a
title: Adversarial Latent Autoencoders
authors: Stanislav Pidhorskyi
abstract: Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.04467.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fc9b27532b2bd365cb4a37a-1607053949953/hls-5fc9b27532b2bd365cb4a37a.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 22/3374 [00:45<1:56:16, 2.08s/it]id: 5fc9afbf32b2bd365cb4a379
title: GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
authors: Prune Truong
abstract: Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addressed with specialized network architectures, designed for only one particular task. This severely limits the generalization capabilities of such networks to new scenarios, where e.g. robustness to larger displacements or higher accuracy is required.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.05524.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fc9afbf32b2bd365cb4a379-1607053265996/hls-5fc9afbf32b2bd365cb4a379.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 23/3374 [00:49<2:26:32, 2.62s/it]id: 5fc9ade232b2bd365cb4a378
title: G-TAD: Sub-Graph Localization for Temporal Action Detection
authors: Mengmeng Xu
abstract: Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3, it obtains an average mAP of 34.09%; on THUMOS14, it reaches 51.6% at [email protected] when combined with a proposal processing method.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.11462.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fc9ade232b2bd365cb4a378-1607052785912/hls-5fc9ade232b2bd365cb4a378.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 24/3374 [00:51<2:19:16, 2.49s/it]id: 5fb8691f873bef099fd89134
title: TBT: Targeted Neural Network Attack With Bit Trojan
authors: Adnan Rakin
abstract: ecurity of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only through specific input patterns (i.e, trigger) during inference. In this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which can insert a targeted neural Trojan into a DNN through the bit-flip attack. Our algorithm efficiently generates a trigger specifically designed to locate certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The objective is that once the attacker flips these vulnerable bits, the network still operates with normal inference accuracy with benign input. However, when the attacker activates the trigger by embedding it with any input, the network is forced to classify all inputs to a certain target class. We demonstrate that flipping only several vulnerable bits identified by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan-infected model. We perform extensive experiments of CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18 architectures. Our proposed TBT could classify 92 % of test images to a target class with as little as 84 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1909.05193.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fb8691f873bef099fd89134-1605921068185/hls-5fb8691f873bef099fd89134.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 25/3374 [00:53<2:13:42, 2.40s/it]id: 5fb8679a6255f25b0db868e0
title: PolyTransform: Deep Polygon Transformer for Instance Segmentation
authors: Justin Liang
abstract: In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods. In particular, we first exploit a segmentation network to generate instance masks. We then convert the masks into a set of polygons that are then fed to a deforming network that transforms the polygons such that they better fit the object boundaries. Our experiments on the challenging Cityscapes dataset show that our PolyTransform significantly improves the performance of the backbone instance segmentation network and ranks 1st on the Cityscapes test-set leaderboard. We also show impressive gains in the interactive annotation setting.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.02801.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fb8679a6255f25b0db868e0-1605920681837/hls-5fb8679a6255f25b0db868e0.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 26/3374 [00:56<2:07:30, 2.29s/it]id: 5fb5f14c620b3c4fbbf0ba03
title: Multi-Scale Interactive Network for Salient Object Detection
authors: Youwei Pang
abstract: Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utilization of multi-level and multi-scale features. In this paper, we propose the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates. To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit. Besides, the class imbalance issue caused by the scale variation weakens the effect of the binary cross entropy loss and results in the spatial inconsistency of the predictions. Therefore, we exploit the consistency-enhanced loss to highlight the fore-/back-ground difference and preserve the intra-class consistency. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fb5f14c620b3c4fbbf0ba03-1605759320314/hls-5fb5f14c620b3c4fbbf0ba03.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/lartpang/MINet
save successfully!
1%| | 27/3374 [00:58<2:04:27, 2.23s/it]id: 5fb5e8a5b4a088136ddfb12c
title: Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations
authors: Chaoning Zhang
abstract: A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a vector for feature representation, and exploit them to analyze the mutual influence of two independent inputs based on the Pearson correlation coefficient (PCC). We utilize this vector representation to understand adversarial examples by disentangling the clean images and adversarial perturbations, and analyze their influence on each other. Our results suggest a new perspective towards the relationship between images and universal perturbations: Universal perturbations contain dominant features, and images behave like noise to them. This feature perspective leads to a new method for generating targeted universal adversarial perturbations using random source images. We are the first to achieve the challenging task of a targeted universal attack without utilizing original training data. Our approach using a proxy dataset achieves comparable performance to the state-of-the-art baselines which utilize the original training dataset.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2007.06189.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fb5e8a5b4a088136ddfb12c-1605757107659/hls-5fb5e8a5b4a088136ddfb12c.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 28/3374 [01:00<2:04:57, 2.24s/it]id: 5fb30a3657be5906af4767c3
title: Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
authors: Fengda Zhu
abstract: Vision-Language Navigation (VLN) is a task where agents learn to navigate following natural language instructions. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches exploit the vision and language features in cross-modal grounding. However, the VLN task remains challenging, since previous works have neglected the rich semantic information contained in the environment (such as implicit navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, estimating the navigation progress, predicting the next orientation, and evaluating the trajectory consistency. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activity and build a thorough perception of the environment. Our experiments indicate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. Empirically, we demonstrate that an agent trained with self-supervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.07883.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fb30a3657be5906af4767c3-1605569098862/hls-5fb30a3657be5906af4767c3.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 29/3374 [01:02<2:03:36, 2.22s/it]id: 5faacc238d3d32d3df66c62f
title: SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
authors: Yue Jiang
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.07109.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5faacc238d3d32d3df66c62f-1605028924571/hls-5faacc238d3d32d3df66c62f.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 30/3374 [01:04<1:59:58, 2.15s/it]id: 5faae1cee4d4313144af915b
title: Hierarchical Conditional Relation Networks for Video Question Answering
authors: Thao Minh Le
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2002.10698.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5faae1cee4d4313144af915b-1605034468780/hls-5faae1cee4d4313144af915b.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 31/3374 [01:06<1:59:41, 2.15s/it]id: 5faa0bd8952f54044ed26c19
title: 4D Visualization of Dynamic Events From Unconstrained Multi-View Videos
authors: Aayush Bansal
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faa0bd8952f54044ed26c19-1604979706114/hls-5faa0bd8952f54044ed26c19.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 32/3374 [01:08<1:57:31, 2.11s/it]id: 5faf26c418656e0a4edac667
title: Searching Central Difference Convolutional Networks for Face Anti-Spoofing
authors: Zitong Yu
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf26c418656e0a4edac667-1605314277599/hls-5faf26c418656e0a4edac667.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 33/3374 [01:10<1:57:11, 2.10s/it]id: 5faf23f1813f63bf087d8e12
title: Set-Constrained Viterbi for Set-Supervised Action Segmentation
authors: Jun Li
abstract: This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering. Prior work typically uses a classifier that independently labels video frames for generating the pseudo ground truth, and multiple instance learning for training the classifier. We extend this framework by specifying an HMM, which accounts for co-occurrences of action classes and their temporal lengths, and by explicitly training the HMM on a Viterbi-based loss. Our first contribution is the formulation of a new set-constrained Viterbi algorithm (SCV). Given a video, the SCV generates the MAP action segmentation that satisfies the ground truth. This prediction is used as a framewise pseudo ground truth in our HMM training. Our second contribution in training is a new regularization of feature affinities between training videos that share the same action classes. Evaluation on action segmentation and alignment on the Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our significant performance improvement for the two tasks over prior work.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2002.11925.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf23f1813f63bf087d8e12-1605313534342/hls-5faf23f1813f63bf087d8e12.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 34/3374 [01:13<2:03:40, 2.22s/it]id: 5fa9e79ea1025066c24d6ffc
title: A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
authors: Edward Kim
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.00289.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e79ea1025066c24d6ffc-1604970420885/hls-5fa9e79ea1025066c24d6ffc.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 35/3374 [01:15<1:58:24, 2.13s/it]id: 5faf2093f1baf4bffa4f9f29
title: EPOS: Estimating 6D Pose of Objects with Symmetries
authors: Tomas Hodan
abstract: We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.00605.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf2093f1baf4bffa4f9f29-1605312669634/hls-5faf2093f1baf4bffa4f9f29.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 36/3374 [01:22<3:21:02, 3.61s/it]id: 5faf1fa90cab41ba9b0273ce
title: Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection
authors: Pengkai Zhu
abstract: Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable. While vanilla deep neural networks deliver high performance for objects available during training, unseen object detection degrades significantly. At a fundamental level, while vanilla detectors are capable of proposing bounding boxes, which include unseen objects, they are often incapable of assigning high-confidence to unseen objects, due to the inherent precision/recall tradeoffs that requires rejecting background objects. We propose a novel detection algorithm Dont Even Look Once (DELO), that synthesizes visual features for unseen objects and augments existing training algorithms to incorporate unseen object detection. Our proposed scheme is evaluated on Pascal VOC and MSCOCO, and we demonstrate significant improvements in test accuracy over vanilla and other state-of-art zero-shot detectors
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.07933.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf1fa90cab41ba9b0273ce-1605312434923/hls-5faf1fa90cab41ba9b0273ce.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 37/3374 [01:24<2:53:20, 3.12s/it]id: 5faacb2bf20501af96222f96
title: Improving Action Segmentation via Graph-Based Temporal Reasoning
authors: Yifei Huang
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faacb2bf20501af96222f96-1605028663043/hls-5faacb2bf20501af96222f96.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 38/3374 [01:26<2:35:32, 2.80s/it]id: 5faf07cbd8c2c2346af64c0b
title: Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
authors: Zhonghao Wang
abstract: We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. Based on the observation that stuff categories usually share similar appearances across images of different domains while things (i.e. object instances) have much larger differences, we propose to improve the semantic-level alignment with different strategies for stuff regions and for things: 1) for the stuff categories, we generate feature representation for each class and conduct the alignment operation from the target domain to the source domain; 2) for the thing categories, we generate feature representation for each individual instance and encourage the instance in the target domain to align with the most similar one in the source domain. In this way, the individual differences within thing categories will also be considered to alleviate over-alignment. In addition to our proposed method, we further reveal the reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy and show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains. We conduct extensive experiments in two unsupervised domain adaptation tasks, i.e. GTA5 to Cityscapes and SYNTHIA to Cityscapes, and achieve the new state-of-the-art segmentation accuracy.
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf07cbd8c2c2346af64c0b-1605306327937/hls-5faf07cbd8c2c2346af64c0b.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 39/3374 [01:28<2:32:02, 2.74s/it]id: 5faaccf873e1b361fd6a1151
title: Show, Edit and Tell: A Framework for Editing Image Captions
authors: Fawaz Sammani
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faaccf873e1b361fd6a1151-1605029124436/hls-5faaccf873e1b361fd6a1151.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 40/3374 [01:30<2:18:34, 2.49s/it]id: 5faac9fd382c022434757917
title: Varicolored Image De-Hazing
authors: Akshay Dudhane
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faac9fd382c022434757917-1605028360369/hls-5faac9fd382c022434757917.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 41/3374 [01:32<2:11:13, 2.36s/it]id: 5faf026cd12b2544afecb20a
title: DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network
authors: Chun-Hao Liu
abstract: A novel "physics-free" approach of designing indoor radio dot layout for a floor plan is introduced by formulating it as an image-to-image translation problem and solved with customized dimension-aware conditional generative adversarial networks (DA-cGANs). The proposed model generates a desirable radio heatmap and its respective radio dot layout from a given floor plan with wall types, physical dimension, and macro-cell interference, by learning from the accumulated indoor radio designs by human experts. Considering the nature of radio propagation, two new loss functions and a two-stage training strategy are proposed for the generator to learn the right direction of signal propagation and precise dot locations, in addition to a sectional analysis for dealing with large floor plans. Experimental results show that the new model is effectively generating acceptable dot layout designs and that dimension-awareness is a key enabler for this type of prediction.
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5faf026cd12b2544afecb20a-1605304950665/hls-5faf026cd12b2544afecb20a.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%| | 42/3374 [01:34<2:04:37, 2.24s/it]id: 5fa9d52a8a1378120d965136
title: A Multi-Hypothesis Approach to Color Constancy
authors: Daniel Hernández
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2002.12896.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9d52a8a1378120d965136-1604965683584/hls-5fa9d52a8a1378120d965136.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 43/3374 [01:36<1:58:56, 2.14s/it]id: 5fa9d6304b3443f259a4a028
title: HybridPose: 6D Object Pose Estimation Under Hybrid Representations
authors: Chen Song
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2001.01869.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9d6304b3443f259a4a028-1604965946251/hls-5fa9d6304b3443f259a4a028.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 44/3374 [01:38<1:54:27, 2.06s/it]id: 5fa9d8614d098d6bae78274e
title: ILFO: Adversarial Attack on Adaptive Neural Networks
authors: Mirazul Haque
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9d8614d098d6bae78274e-1604966506624/hls-5fa9d8614d098d6bae78274e.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 45/3374 [01:40<1:52:50, 2.03s/it]id: 5fa9d98f0d936da76e994b45
title: Understanding Human Hands in Contact at Internet Scale
authors: nicoleshan1018
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2006.06669.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9d98f0d936da76e994b45-1604966826959/hls-5fa9d98f0d936da76e994b45.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 46/3374 [01:42<1:50:53, 2.00s/it]id: 5fa9d9880d936da76e994b44
title: Quality Guided Sketch-to-Photo Image Synthesis
authors: ucheosahor
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2005.02133.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9d9880d936da76e994b44-1604966828693/hls-5fa9d9880d936da76e994b44.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 47/3374 [01:44<1:52:55, 2.04s/it]id: 5fa9dd78cba45e756faea1d1
title: Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS
authors: Gabriel Bender
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2008.06120.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9dd78cba45e756faea1d1-1604967810346/hls-5fa9dd78cba45e756faea1d1.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 48/3374 [01:46<1:53:16, 2.04s/it]id: 5fa9deb66064a3d8bbc140a0
title: StructEdit: Learning Structural Shape Variations
authors: Kaichun Mo
abstract: ion and transfer. Our approach is based on a conditional variational autoencoder for encoding and decoding shape deltas, conditioned on a source shape. We demonstrate the effectiveness and robustness of our approach in multiple shape modification and generation tasks, and provide comparison and ablation studies on the PartNet dataset, one of the largest publicly available 3D datasets.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.11098.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9deb66064a3d8bbc140a0-1604968130541/hls-5fa9deb66064a3d8bbc140a0.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
1%|▏ | 49/3374 [01:48<1:51:08, 2.01s/it]id: 5fa9ea8819ee2d16769c8460
title: Transform and Tell: Entity-Aware News Image Captioning
authors: Alasdair Tran
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.08070.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9ea8819ee2d16769c8460-1604971157228/hls-5fa9ea8819ee2d16769c8460.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/alasdairtran/transform-and-tell
save successfully!
1%|▏ | 50/3374 [01:50<1:49:14, 1.97s/it]id: 5fa9e96c2dcbf6b2bc92e401
title: Domain Adaptive Image-to-Image Translation
authors: yingcong.ian.chen
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2009.01166.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e96c2dcbf6b2bc92e401-1604970874881/hls-5fa9e96c2dcbf6b2bc92e401.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 51/3374 [01:52<1:50:35, 2.00s/it]id: 5fa9e885e8f5a67b4f28412a
title: Composed Query Image Retrieval Using Locally Bounded Features
authors: Mehrdad Hosseinzadeh
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e885e8f5a67b4f28412a-1604970639700/hls-5fa9e885e8f5a67b4f28412a.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 52/3374 [01:54<1:48:33, 1.96s/it]id: 5fa9e6e832ae12d9b1287252
title: Affinity Graph Supervision for Visual Recognition
authors: Chu Wang
abstract: ing features from such graphs, while the learning of the affinities themselves has been overlooked. Here we propose a principled method to directly supervise the learning of weights in affinity graphs, to exploit meaningful connections between entities in the data source. Applied to a visual attention network, our affinity supervision improves relationship recovery between objects, even without the use of manually annotated relationship labels. We further show that affinity learning between objects boosts scene categorization performance and that the supervision of affinity can also be applied to graphs built from mini-batches, for neural network training. In an image classification task we demonstrate consistent improvement over the baseline, with diverse network architectures and datasets.
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2003.09049.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e6e832ae12d9b1287252-1604970227192/hls-5fa9e6e832ae12d9b1287252.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 53/3374 [01:56<1:50:51, 2.00s/it]id: 5fa9e632c6630690d38bf4fd
title: Grid-GCN for Fast and Scalable Point Cloud Learning
authors: Qiangeng Xu
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1912.02984.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e632c6630690d38bf4fd-1604970049464/hls-5fa9e632c6630690d38bf4fd.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 54/3374 [01:58<1:53:55, 2.06s/it]id: 5fa9e4c93744b24d9c71ee42
title: ReSprop: Reuse Sparsified Backpropagation
authors: Negar Goli
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e4c93744b24d9c71ee42-1604969695527/hls-5fa9e4c93744b24d9c71ee42.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 55/3374 [02:00<1:53:58, 2.06s/it]id: 5fa9e276f1613f16be5d12f6
title: Binarizing MobileNet via Evolution-Based Searching
authors: Hai Phan
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2005.06305.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e276f1613f16be5d12f6-1604969089313/hls-5fa9e276f1613f16be5d12f6.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 56/3374 [02:02<1:55:34, 2.09s/it]id: 5fa9e0b90d8c876c2b1449da
title: Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones
authors: Chenchi Luo
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9e0b90d8c876c2b1449da-1604968648620/hls-5fa9e0b90d8c876c2b1449da.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 57/3374 [02:04<1:54:26, 2.07s/it]id: 5fa9df625401d253379b2de3
title: Bayesian Adversarial Human Motion Synthesis
authors: Rui Zhao
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9df625401d253379b2de3-1604968314953/hls-5fa9df625401d253379b2de3.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 58/3374 [02:06<1:51:21, 2.02s/it]id: 5fa9eb757d13004924849aab
title: Adversarial Examples Improve Image Recognition
authors: Cihang Xie
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/1911.09665.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9eb757d13004924849aab-1604971393864/hls-5fa9eb757d13004924849aab.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 59/3374 [02:08<1:49:59, 1.99s/it]id: 5fa9ecc5cda7745dad9b7afc
title: Straight to the Point: Fast-Forwarding Videos via Reinforcement Learning Using Textual Data
authors: Washington Ramos
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2003.14229.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9ecc5cda7745dad9b7afc-1604971728720/hls-5fa9ecc5cda7745dad9b7afc.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 60/3374 [02:10<1:50:11, 1.99s/it]id: 5fa9ed948db098686a448c38
title: Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians
authors: Jialian Wu
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9ed948db098686a448c38-1604971943802/hls-5fa9ed948db098686a448c38.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 61/3374 [02:12<1:50:05, 1.99s/it]id: 5fa9eec96283055167aada48
title: Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
authors: Yu-Lun Liu
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2004.01179.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9eec96283055167aada48-1604972246648/hls-5fa9eec96283055167aada48.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 62/3374 [02:14<1:50:00, 1.99s/it]id: 5fa9ef8cba03e5ee0cba6087
title: Deep Non-Line-of-Sight Reconstruction
authors: Javier Grau Chopite
abstract:
year: 2020
publicationOrg: CVPR
pdfurl: https://arxiv.org/pdf/2001.09067.pdf
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9ef8cba03e5ee0cba6087-1604972439664/hls-5fa9ef8cba03e5ee0cba6087.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl:
save successfully!
2%|▏ | 63/3374 [02:16<1:48:13, 1.96s/it]id: 5fa9f03b9058c30310387ec2
title: Investigating Loss Functions for Extreme Super-Resolution
authors: Younghyun Jo
abstract:
year: 2020
publicationOrg: CVPR
pdfurl:
pdfPath:
videoUrl: https://stream.crossminds.ai/5fa9f03b9058c30310387ec2-1604972615234/hls-5fa9f03b9058c30310387ec2.m3u8
videoPath:
datasetUrl:
publicationUrl:
codeurl: https://github.com/kingsj0405/ciplab-NTIRE-2020
save successfully!
2%|▏ | 64/3374 [02:18<1:48:14, 1.96s/it]id: 5fa9f16a7e21285f2871f27a
title: D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
authors: Xuyang Bai
abstract:
year: 2020
publicationOrg: CVPR