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Architecture Design for Deep Neural Networks II

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ICME2019 Tutorial: Architecture Design for Deep Neural Networks II

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Architecture Design for Deep Neural Networks II

  1. 1. • ImageNet, GPU 2
  2. 2. 3 Deeper - more layers Go deeper for boosting the capacity deeper
  3. 3. 8 4
  4. 4. 19 5
  5. 5. 22 6
  6. 6. T + H xx 1-T T + H xx 1-T T + H xx 1-T T + H xx 1-T 100+ 7
  7. 7. 152 8
  8. 8. 9
  9. 9. Session 1: Interleaved Group Convolutions for efficient and wider CNNs
  10. 10. 11 Wider - more channels Deeper - more layers Go wider by eliminating redundancy in convolutions → widerdeeper
  11. 11. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 12
  12. 12. 14
  13. 13. 15
  14. 14. 16
  15. 15. 17
  16. 16. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 18
  17. 17. • Integer • Quantization 19
  18. 18. • Binarization • Quantization 20
  19. 19. • Binarization • Integer 21
  20. 20. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 22
  21. 21. • Filter pruning • Channel pruning 23
  22. 22. • Filter pruning • Channel pruning 24
  23. 23. • Filter pruning • Channel pruning 25
  24. 24. • Filter pruning • Channel pruning 26
  25. 25. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 27
  26. 26. 28
  27. 27. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 29
  28. 28. • Non-structured sparse • Structured sparse 30
  29. 29. • Non-structured sparse • Structured sparse 31
  30. 30. • Convolution operations • Low-precision kernels • Low-rank kernels • Composition from low-rank kernels • Sparse kernels • Composition from sparse kernels 32
  31. 31. [1] Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang: Interleaved Group Convolutions. ICCV 2017: 4383-4392 [2] Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, and Guo-JunQi. IGCV2: Interleaved Structured Sparse Convolutional Neural Networks. CVPR 2018. [3] Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. BMVC 2018. 33
  32. 32. IGCV1: Interleaved group convolutions for large models Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang: Interleaved Group Convolutions, ICCV 2017. 34
  33. 33. Conv Channel 35
  34. 34. Split Concat. Conv. Conv. 36
  35. 35. Split Concat. Conv. Conv. Primary group convolution Split Conv. Conv. Conv. Concat. Secondary group 1 × 1 convolution L=2 primary partitions 3 channels in each partition M=3 secondary partitions 2 channels in each partition 37
  36. 36. The path connecting each input channel with each output channel Split Concat. Conv. Conv. Split Conv. Conv. Conv. Concat. 38
  37. 37. Split Concat. Conv. Conv. Split Conv. Conv. Conv. Concat. x′ = PW 𝑑 P 𝑇 W 𝑝 x x W 𝑝 P 𝑇 W 𝑑 P x′ 39
  38. 38. x′ = PW 𝑑 P 𝑇 W 𝑝 x dense 40
  39. 39. Advantages: Wider than regular convolutions Thus, our IGC is wider except 𝐿 = 1 under the same #parameters 41
  40. 40. depth RegConv-18 IGC 20 92.55 ± 0.14 92.84 ± 0.26 38 91.57 ± 0.09 92.24 ± 0.62 62 88.60 ± 0.49 90.03 ± 0.85 depth RegConv-18 IGC 20 0.34 0.15 38 0.71 0.31 62 1.20 0.52 depth RegConv-18 IGC 20 0.51 0.29 38 1.1 0.57 62 1.7 0.95 +1.43 42
  41. 41. depth RegConv-18 IGC 20 68.71 ± 0.32 70.54 ± 0.26 38 65.00 ± 0.57 69.56 ± 0.76 62 58.52 ± 2.31 65.84 ± 0.75 depth RegConv-18 IGC 20 0.34 0.15 38 0.71 0.31 62 1.20 0.52 depth RegConv-18 IGC 20 0.51 0.29 38 1.1 0.57 62 1.7 0.95 +7.32 43
  42. 42. #params FLOPS Training error Validation error Top-1 Top-5 Top-1 Top-5 ResNet (Reg. Conv.) 1.133 2.1 21.43 5.96 30.58 10.77 Our approach 0.861 1.3 13.93 2.75 26.95 8.92 +3.63 +1.85 44
  43. 43. • Primary group convolution • Secondary group convolution Regular convolutions are interleaved group convolutions Primary Secondary W11 W12 W21 W22 + + W 45
  44. 44. Split Concat. Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. 47
  45. 45. 23 32 39 50 54 72 84 80 64 44 62 88 124 154 184 196 205 192 170 128width M=2: Higher accuracy than Xception Best accuracy 48
  46. 46. Xception Our approach testing error 36.93 ± 0.54 36.11 ± 0.62 #params 3.62 × 104 3.80 × 104 FLOPS 3.05 × 107 3.07 × 107 Xception Our approach testing error 32.87 ± 0.67 31.87 ± 0.58 #params 1.21 × 105 1.26 × 105 FLOPS 1.11 × 108 1.12 × 108 −0.82 −1.00 49
  47. 47. Split Conv. Conv. Conv. Conv. Conv. Concat Split Conv. Conv. Conv. Concat 50
  48. 48. 51
  49. 49. IGCV2: Interleaved Structured Sparse Convolutional Neural Networks Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, and Guo-JunQi. IGCV2: Interleaved Structured Sparse Convolutional Neural Networks. CVPR 2018. 53
  50. 50. small 54
  51. 51. Split Conv. Conv. Conv. Conv. Split Conv. Conv. ConcatConcat 55
  52. 52. Split Conv. Conv. Conv. Conv. Split Conv. Conv. ConcatConcat IGCV1 IGCV1 56
  53. 53. Split Conv. Conv. Conv. Conv. Split Conv. Conv. Split Conv. Conv. Split Concat Concat Concat Concat Conv. IGCV1 57
  54. 54. Split Conv. Conv. Conv. Conv. Split Conv. Conv. Split Conv. Conv. Split Concat Conv. Conv. Split Conv. Conv. Split Concat Concat Concat Concat Concat 58
  55. 55. Split Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. Conv. ConcatSplitConcat SplitConcat 𝐿 (e.g., 3 or >3) group convolutionsx′ = P3W3P2W2P1W1x W3W1 P1 W2 P2 P3x Dense due to complementary condition 59
  56. 56. Design: Balance condition minimum total #parameters, three conditions hold: 1) #parameters for (𝐿 − 1) group convolutions are the same 2) #branches for (𝐿 − 1) group convolutions are the same 3) #channels in each branch are the same How to design 𝐿 group convolutions #channels in each branch: 𝐾2 = ⋯ = 𝐾𝐿 = 𝐶 1 𝐿−1 = 𝐾 width IGC block: 1 channel-wise 3 × 3 convolution + (𝐿 − 1) group 1 × 1 convolutions 61
  57. 57. Meet complementary and balance conditions 62
  58. 58. #parameters When balance condition holds 3 × 3 1 × 1 How many group convolutions? 𝐿 = log 𝐶 + 1 so that 𝑄 is minimum Benefit: Further redundancy reduction by composing more (𝐿 > 2) sparse matrices 𝑄 𝐿 = 3 < 𝑄 𝐿 = 2 if 𝐶 > 4 64
  59. 59. 65
  60. 60. Depth IGCV1 IGCV2 8 66.52 ± 0.12 67.65 ± 0.29 20 70.87 ± 0.39 72.63 ± 0.07 26 71.82 ± 0.40 73.49 ± 0.34 Depth IGCV1 IGCV2 8 0.046 0.046 20 0.151 0.144 26 0.203 0.193 Depth IGCV1 IGCV2 8 1.00 1.18 20 2.89 3.20 26 3.83 4.21 +1.67 66
  61. 61. Depth IGCV1 IGCV2 8 48.57 ± 0.53 51.49 ± 0.33 20 54.83 ± 0.27 56.40 ± 0.17 26 56.64 ± 0.15 57.12 ± 0.09 Depth IGCV1 IGCV2 8 0.046 0.046 20 0.151 0.144 26 0.203 0.193 Depth IGCV1 IGCV2 8 1.00 1.18 20 2.89 3.20 26 3.83 4.21 +0.48 67
  62. 62. • Complementary condition • Strict → loose small 69
  63. 63. Strict one and only one 70
  64. 64. Strict is one and only one path ˄ Loose are more paths rich 71
  65. 65. Strict channels lie in different branches and super-channels Loose 72
  66. 66. small IGCV2 73
  67. 67. Model #Params. (M) FLOPS (M) Accuracy (%) MobileNetV1-1.0 4.2 569 70.6 IGCV2-1.0 4.1 564 70.7 MobileNetV1-0.5 1.3 149 63.7 IGCV2-0.5 1.3 156 65.5 MobileNetV1-0.25 0.5 41 50.6 IGCV2-0.25 0.5 46 54.9 75
  68. 68. IGCV3: Interleaved Low-Rank Group Convolutions Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. BMVC 2018. 76
  69. 69. • Low-rank structured sparse matrix composition • Structured sparse + low-rank small 77
  70. 70. 1 × 1 Gconv. 3 × 3 Cconv. 1 × 1 Gconv. Channel Gconv 3 × 3 Cconv Gcon 78
  71. 71. Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. BMVC 2018. small IGCV3 79
  72. 72. 81
  73. 73. 82
  74. 74. 83
  75. 75. • Small • Fast • High Summary 84
  76. 76. 87 MobileNet IGC ShuffleNet v1 v1/v2 ≈ v1 Block Depthwise + pointwise 2/2+ group conv. 2 group conv. Relation Complementary Complementary Shuffled Experiment Strong Weak Strong Insight Weak Strong Weak v2 v3 v2 Block Depthwise + low rank pointwise 2 group + low-rank pointwise Relation Complementary Complementary Experiment Strong Strong Insight Weak Strong
  77. 77. • Ting Zhang 88
  78. 78. IGC Code: https://github.com/homles11/IGCV3 89
  79. 79. Session 2: High-Resolution Representation Learning Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang: Deep High-Resolution Representation Learning for Human Pose Estimation. CVPR 2019 Ke Sun, Yang Zhao, Borui Jiang, Tianheng Cheng, Bin Xiao, Dong Liu, Yadong Mu, Xinggang Wang, Wenyu Liu, Jingdong Wang: High-Resolution Representation Learning for labeling pixels and regions (Journal extension).
  80. 80. 92 Wider - more channels Deeper - more layers Finer - higher resolution → finer New dimension: go finer towards high-resolution representation learning deeper → wider
  81. 81. 93 32 × 32 5 × 5 28 × 28 14 × 14 10 × 10 1/6 series High-resolution conv. → medium-resolution conv. → low-resolution conv. Low-resolution and other classification networks: AlexNet, VGGNet, GoogleNet, ResNet, DenseNet, ……
  82. 82. 94 Low resolution is enough image recog. pixel-level recog.region-level recog. The high-resolution representation is needed global position-sensitive
  83. 83. 97 High-resolution low-resolution classification networks ❑ Recover Hourglass, U-Net, Encoder-decoder, DeconvNet, SimpleBaseline, etc
  84. 84. 98 U-Net SegNet DeconvNet Hourglass Look different, essentially the same
  85. 85. 99 High-resolution low-resolution classification networks ❑ Recover location-sensitivity loss Hourglass, U-Net, Encoder-decoder, DeconvNet, SimpleBaseline, etc
  86. 86. 102 Learn high-resolution representations through high resolution maintenance rather than recovering High-resolution
  87. 87. 103 series
  88. 88. 104 parallel with repeated fusions
  89. 89. 105 parallel repeated fusions
  90. 90. 106
  91. 91. 107 series • Recover from low-resolution representations • Repeat fusions across resolutions to strengthen high- & low-resolution representations parallel Maintain through the whole process HRNet can learn high-resolution strong representations
  92. 92. 108 #blocks = 1 #blocks = 4 #blocks = 3
  93. 93. 109 Image classification Semantic segmentation Object detection Face alignment Pose estimation
  94. 94. 111
  95. 95. 112
  96. 96. 113 Datasets training validation testing Evaluation COCO 2017 57K 5000 images 20K AP@OKS MPII 13K 12k PCKh PoseTrack 292 videos 50 208 mAP/MOTA COCO: http://cocodataset.org/#keypoints-eval MPII http://human-pose.mpi-inf.mpg.de/ PoseTrack https://posetrack.net/
  97. 97. 114
  98. 98. 115 Method Backbone Pretrain Input size #Params GFLOPs AP AP50 AP75 APM APL AR 8-stage Hourglass [38] 8-stage Hourglass N 256×192 25.1M 14.3 66.9 - - - - - CPN [11] ResNet-50 Y 256×192 27.0M 6.2 68.6 - - - - - CPN+OHKM [11] ResNet-50 Y 256×192 27.0M 6.2 69.4 - - - - - SimpleBaseline [66] ResNet-50 Y 256×192 24.0M 8.9 70.4 88.6 78.3 67.1 77.2 76.3 SimpleBaseline [66] ResNet-101 Y 256×192 50.3M 12.4 71.4 89.3 79.3 68.1 78.1 77.1 HRNet-W32 HRNet-W32 N 256×192 28.5M 7.1 73.4 89.5 80.7 70.2 80.1 78.9 HRNet-W32 HRNet-W32 Y 256×192 28.5M 7.1 74.4 90.5 81.9 70.8 81.0 79.8 SimpleBaseline [66] ResNet-152 Y 256×192 68.6M 15.7 72.0 89.3 79.8 68.7 78.9 77.8 HRNet-W48 HRNet-W48 Y 256×192 63.6M 14.6 75.1 90.6 82.2 71.5 81.8 80.4 SimpleBaseline [66] ResNet-152 Y 384×288 68.6M 35.6 74.3 89.6 81.1 70.5 79.7 79.7 HRNet-W32 HRNet-W32 Y 384×288 28.5M 16.0 75.8 90.6 82.7 71.9 82.8 81.0 HRNet-W48 HRNet-W48 Y 384×288 63.6M 32.9 76.3 90.8 82.9 72.3 83.4 81.2
  99. 99. 116 method Backbone Input size #Params GFLOPs AP AP50 AP75 APM APL AR Bottom-up: keypoint detection and grouping OpenPose [6], CMU - - - - 61.8 84.9 67.5 57.1 68.2 66.5 Associative Embedding [39] - - - - 65.5 86.8 72.3 60.6 72.6 70.2 PersonLab [46], Google - - - - 68.7 89.0 75.4 64.1 75.5 75.4 MultiPoseNet [33] - - - - 69.6 86.3 76.6 65.0 76.3 73.5 Top-down: human detection and single-person keypoint detection Mask-RCNN [21], Facebook ResNet-50-FPN - - - 63.1 87.3 68.7 57.8 71.4 - G-RMI [47] ResNet-101 353×257 42.0M 57.0 64.9 85.5 71.3 62.3 70.0 69.7 Integral Pose Regression [60] ResNet-101 256×256 45.0M 11.0 67.8 88.2 74.8 63.9 74.0 - G-RMI + extra data [47] ResNet-101 353×257 42.6M 57.0 68.5 87.1 75.5 65.8 73.3 73.3 CPN [11] , Face++ ResNet-Inception 384×288 - - 72.1 91.4 80.0 68.7 77.2 78.5 RMPE [17] PyraNet [77] 320×256 28.1M 26.7 72.3 89.2 79.1 68.0 78.6 - CFN [25] , - - - - 72.6 86.1 69.7 78.3 64.1 - CPN (ensemble) [11], Face++ ResNet-Inception 384×288 - - 73.0 91.7 80.9 69.5 78.1 79.0 SimpleBaseline [72], Microsoft ResNet-152 384×288 68.6M 35.6 73.7 91.9 81.1 70.3 80.0 79.0 HRNet-W32 HRNet-W32 384×288 28.5M 16.0 74.9 92.5 82.8 71.3 80.9 80.1 HRNet-W48 HRNet-W48 384×288 63.6M 32.9 75.5 92.5 83.3 71.9 81.5 80.5 HRNet-W48 + extra data HRNet-W48 384×288 63.6M 32.9 77.0 92.7 84.5 73.4 83.1 82.0
  100. 100. 117
  101. 101. 121 https://posetrack.net/leaderboard.php by Feb. 28, 2019 PoseTrack Leaderboard Multi-Person Pose TrackingMulti-Frame Person Pose Estimation
  102. 102. 125 COCO, train from scratch Method Final exchange Int. exchange across Int. exchange within AP (a) ✓ 70.8 (b) ✓ ✓ 71.9 (c) ✓ ✓ ✓ 73.4
  103. 103. 126 COCO, train from scratch
  104. 104. 128 Image classification Semantic segmentation Object detection Face alignment Pose estimation
  105. 105. 130
  106. 106. 131 Regular convolution Multi-resolution convolution (across-resolution fusion)
  107. 107. 132 Datasets training testing #landmarks Evaluation (NME) WFLW 7500 2500 98 inter-ocular AFLW 20000 full 4386, frontal 1314 19 box COFW 1345 507 29 inter-ocular 300W 3148 full 689, challenging 135, test 600 68 inter-ocular
  108. 108. 133 backbone Test Pose Expr Illu Mu Occu Blur ESR [10] - 11.13 25.88 11.47 10.49 11.05 13.75 12.20 SDM [105], CMU - 10.29 24.10 11.45 9.32 9.38 13.03 11.28 CFSS [132] - 9.07 21.36 10.09 8.30 8.74 11.76 9.96 DVLN [100] VGG-16 6.08 11.54 6.78 5.73 5.98 7.33 6.88 Our approach HRNetV2-W18 4.60 7.94 4.85 4.55 4.29 5.44 5.42
  109. 109. 135 backbone full frontal RCN [39] - 5.60 5.36 CDM [113] - 5.43 3.77 ERT [43] - 4.35 2.75 LBF [79] - 4.25 2.74 SDM [105], CMU - 4.05 2.94 CFSS [131] - 3.92 2.68 RCPR [8] - 3.73 2.87 CCL [132] - 2.72 2.17 DAC-CSR [29] - 2.27 1.81 TSR [68] VGG-S 2.17 - CPM + SBR [25] CPM 2.14 - SAN [24] ResNet-152 1.91 1.85 DSRN [69] - 1.86 - LAB (w/o B) [99], SenseTime Hourglass 1.85 1.62 Our approach HRNetV2-W18 1.57 1.46
  110. 110. 137 backbone NME FR0.1 Human - 5.60 - ESR [10] - 11.20 36.00 RCPR [8] - 8.50 20.00 HPM [32] 7.50 13.00 CCR [27] - 7.03 10.90 DRDA [115] - 6.46 6.00 RAR [102] - 6.03 4.14 DAC-CSR [29] - 6.03 4.73 LAB (w/o B) [99], SenseTime Hourglass 5.58 2.76 Our approach HRNetV2-W18 3.45 0.19
  111. 111. 139 backbone common challenging full RCN [39] - 4.67 8.44 5.41 DSRN [69] - 4.12 9.68 5.21 PCD-CNN [50] - 3.67 7.62 4.44 CPM + SBR [25] CPM 3.28 7.58 4.10 SAN [24] ResNet-152 3.34 6.60 3.98 DAN [49] - 3.19 5.24 3.59 Our approach HRNetV2-W18 2.87 5.15 3.32
  112. 112. 141 backbone NME AUC0.08 AUC0.1 FR0.08 FR0.1 Balt. et al. [3] - - 19.55 - 38.83 - ESR [10] - 8.47 26.09 - 30.50 - ERT [43] - 8.41 27.01 - 28.83 - LBF [79] - 8.57 25.27 - 33.67 - Face++ [127] - - 32.81 - 13.00 - SDM [105], CMU - 5.83 36.27 - 13.00 - CFAN [116] - 5.78 34.78 - 14.00 - Yan et al. [108] - 5.74 34.97 - 12.67 - CFSS [131] - 4.78 36.58 - 12.33 - MDM [92] - 4.30 45.32 - 6.80 - DAN [49] - 3.96 47.00 - 2.67 - Chen et al. [17] Hourglass - 53.64 - 2.50 - Deng et al. [21] - - - 47.52 - 5.50 Fan et al. [26] - - - 48.02 - 14.83 Dreg + MDM [35] ResNet101 - - 52.19 - 3.67 JMFA [22] Hourglass - - 54.85 - 1.00 Our approach HRNetV2-W18 3.85 52.09 61.55 1.00 0.33
  113. 113. 143 Image classification Semantic segmentation Object detection Face alignment Pose estimation
  114. 114. 145
  115. 115. 146 Datasets training validation testing #classes Evaluation Cityscapes 2975 500 1525 19+1 mIoU PASCAL context 4998 5105 59+1 mIoU LIP 30462 10000 19+1 mIoU
  116. 116. 147 backbone #Params. GFLOPs mIoU U-Net++ [130] ResNet-101 59.5M 748.5 75.5 DeepLabv3 [14], Google Dilated-resNet-101 58.0M 1778.7 78.5 DeepLabv3+ [16], Google Dilted-Xception-71 43.5M 1444.6 79.6 PSPNet [123], SenseTime Dilated-ResNet-101 65.9M 2017.6 79.7 Our approach HRNetV2-W40 45.2M 493.2 80.2 Our approach HRNetV2-W48 65.9M 747.3 81.1
  117. 117. 148 backbone mIoU iIoU cat. IoU cat. iIoU cat. Model learned on the train+valid set GridNet [130] - 69.5 44.1 87.9 71.1 LRR-4x [33] - 69.7 48.0 88.2 74.7 DeepLab [13], Google Dilated-ResNet-101 70.4 42.6 86.4 67.7 LC [54] - 71.1 - - - Piecewise [60] VGG-16 71.6 51.7 87.3 74.1 FRRN [77] - 71.8 45.5 88.9 75.1 RefineNet [59] ResNet-101 73.6 47.2 87.9 70.6 PEARL [42] Dilated-ResNet-101 75.4 51.6 89.2 75.1 DSSPN [58] Dilated-ResNet-101 76.6 56.2 89.6 77.8 LKM [75] ResNet-152 76.9 - - - DUC-HDC [97] - 77.6 53.6 90.1 75.2 SAC [117] Dilated-ResNet-101 78.1 - - - DepthSeg [46] Dilated-ResNet-101 78.2 - - - ResNet38 [101] WResNet-38 78.4 59.1 90.9 78.1 BiSeNet [111] ResNet-101 78.9 - - - DFN [112] ResNet-101 79.3 - - - PSANet [125], SenseTime Dilated-ResNet-101 80.1 - - - PADNet [106] Dilated-ResNet-101 80.3 58.8 90.8 78.5 DenseASPP [124] WDenseNet-161 80.6 59.1 90.9 78.1 Our approach HRNetV2-w48 81.6 61.8 92.1 82.2
  118. 118. 149 backbone mIoU (59classes) mIoU (60classes) FCN-8s [86] VGG-16 - 35.1 BoxSup [20] - - 40.5 HO_CRF [1] - - 41.3 Piecewise [60] VGG-16 - 43.3 DeepLabv2 [13], Google Dilated-ResNet-101 - 45.7 RefineNet [59] ResNet-152 - 47.3 U-Net++ [130] ResNet-101 47.7 - PSPNet [123], SenseTime Dilated-ResNet-101 47.8 - Ding et al. [23] ResNet-101 51.6 - EncNet [114] Dilated-ResNet-101 52.6 - Our approach HRNetV2-W48 54.0 48.3
  119. 119. 150 backbone extra pixel acc. avg. acc. mIoU Attention+SSL [34] VGG-16 Pose 84.36 54.94 44.73 DeepLabv2 [16], Google Dilated-ResNet-101 - 84.09 55.62 44.80 MMAN[67] Dilated-ResNet-101 - - - 46.81 SS-NAN [125] ResNet-101 Pose 87.59 56.03 47.92 MuLA [72] Hourglass Pose 88.50 60.50 49.30 JPPNet [57] Dilated-ResNet-101 Pose 86.39 62.32 51.37 CE2P [65] Dilated-ResNet-101 Edge 87.37 63.20 53.10 Our approach HRNetV2-W48 N 88.21 67.43 55.90
  120. 120. 151 Image classification Semantic segmentation Object detection Face alignment Pose estimation
  121. 121. 153
  122. 122. 156 backbone Size LS AP AP50 AP75 APS APM APL DFPR [47] ResNet-101 512 1 × 34.6 54.3 37.3 - - - PFPNet [45] VGG16 512 - 35.2 57.6 37.9 18.7 38.6 45.9 RefineDet [118] ResNet-101-FPN 512 - 36.4 57.5 39.5 16.6 39.9 51.4 RelationNet [40] ResNet-101 600 - 39.0 58.6 42.9 - - - C-FRCNN [18] ResNet-101 800 1 × 39.0 59.7 42.8 19.4 42.4 53.0 RetinaNet [62] ResNet-101-FPN 800 1.5 × 39.1 59.1 42.3 21.8 42.7 50.2 Deep Regionlets [107] ResNet-101 800 1.5 × 39.3 59.8 - 21.7 43.7 50.9 FitnessNMS [94] ResNet-101 768 39.5 58.0 42.6 18.9 43.5 54.1 DetNet [56] DetNet-59-FPN 800 2 × 40.3 62.1 43.8 23.6 42.6 50.0 CornerNet [51] Hourglass-104 511 40.5 56.5 43.1 19.4 42.7 53.9 M2Det [126] VGG16 800 ∼ 10 × 41.0 59.7 45.0 22.1 46.5 53.8 Faster R-CNN [61] ResNet-101-FPN 800 1 × 39.3 61.3 42.7 22.1 42.1 49.7 Faster R-CNN HRNetV2p-W32 800 1 × 39.5 61.2 43.0 23.3 41.7 49.1 Faster R-CNN [61] ResNet-101-FPN 800 2 × 40.3 61.8 43.9 22.6 43.1 51.0 Faster R-CNN HRNetV2p-W32 800 2 × 41.1 62.3 44.9 24.0 43.1 51.4 Faster R-CNN [61] ResNet-152-FPN 800 2 × 40.6 62.1 44.3 22.6 43.4 52.0 Faster R-CNN HRNetV2p-W40 800 2 × 42.1 63.2 46.1 24.6 44.5 52.6 Faster R-CNN [11] ResNeXt-101-64x4d-FPN 800 2 × 41.1 62.8 44.8 23.5 44.1 52.3 Faster R-CNN HRNetV2p-W48 800 2 × 42.4 63.6 46.4 24.9 44.6 53.0 Cascade R-CNN [9]* ResNet-101-FPN 800 ∼ 1.6 × 42.8 62.1 46.3 23.7 45.5 55.2 Cascade R-CNN ResNet-101-FPN 800 ∼ 1.6 × 43.1 61.7 46.7 24.1 45.9 55.0 Cascade R-CNN HRNetV2p-W32 800 ∼ 1.6 × 43.7 62.0 47.4 25.5 46.0 55.3
  123. 123. 158 backbone LS mask bbox AP APS APM APL AP APS APM APL ResNet-50-FPN 1 × 34.2 15.7 36.8 50.2 37.8 22.1 40.9 49.3 HRNetV2p-W18 1 × 33.8 15.6 35.6 49.8 37.1 21.9 39.5 47.9 ResNet-50-FPN 2 × 35.0 16.0 37.5 52.0 38.6 21.7 41.6 50.9 HRNetV2p-W18 2 × 35.3 16.9 37.5 51.8 39.2 23.7 41.7 51.0 ResNet-101-FPN 1 × 36.1 16.2 39.0 53.0 40.0 22.6 43.4 52.3 HRNetV2p-W32 1 × 36.7 17.3 39.0 53.0 40.9 24.5 43.9 52.2 ResNet-101-FPN 2 × 36.7 17.0 39.5 54.8 41.0 23.4 44.4 53.9 HRNetV2p-W32 2 × 37.6 17.8 40.0 55.0 42.3 25.0 45.4 54.9
  124. 124. 159 Image classification Semantic segmentation Object detection Face alignment Pose estimation
  125. 125. 160
  126. 126. 161 #Params. GFLOPs Top-1 err. Top-5 err. Residual branch formed by two 3 × 3 convolutions ResNet-38 28.3M 3.80 24.6% 7.4% HRNet-W18 21.3M 3.99 23.1% 6.5% ResNet-71 48.4M 7.46 23.3% 6.7% HRNet-W30 37.7M 7.55 21.9% 5.9% ResNet-105 64.9M 11.1 22.7% 6.4% HRNet-W40 57.6M 11.8 21.1% 5.6% Residual branch formed a bottleneck ResNet-50 25.6M 3.82 23.3% 6.6% HRNet-W44 21.9M 3.90 23.0% 6.5% ResNet-101 44.6M 7.30 21.6% 5.8% HRNet-W76 40.8M 7.30 21.5% 5.8% ResNet-152 60.2M 10.7 21.2% 5.7% HRNet-W96 57.5M 10.2 21.0% 5.7% Surprisingly, HRNet performs slightly better than ResNet
  127. 127. 163
  128. 128. 164 Cityscapes and pascal context COCO detection
  129. 129. 165 vs vs
  130. 130. 166 image-level pixel-levelregion-level Low resolution High resolution Recover from low-resolution (ResNet, VGGNet)  High-resolution (our HRNet) ✓ vs
  131. 131. 167 vs vs
  132. 132. 168 Convolutional neural fabrics Gridnet: generalized U-NetInterlinked CNN Multi-scale densenet
  133. 133. 169 by Google Related to HRNet, but no high-resolution maintenance
  134. 134. 171 Image classification Semantic segmentation Object detection Face alignment Pose estimation and …
  135. 135. 172
  136. 136. 173
  137. 137. 174 Super-resolution from LapSRN Optical flow Depth estimation Edge detection
  138. 138. 175
  139. 139. 176 Used in many challenges in CVPR 2019
  140. 140. 177 , CVPRW 2019 Meitu (美图) adopted the HRNet
  141. 141. 178 NTIRE 2019 Image Dehazing Challenge Report, CVPRW 2019 Meitu (美图) adopted the HRNet
  142. 142. 179
  143. 143. 180
  144. 144. 181
  145. 145. 182 Cityscapes leaderboard: Rank 1
  146. 146. 183 semantic segmentation, object detection, facial landmark detection, human pose estimation Replace classification networks (e.g., ResNet) for computer vision tasks
  147. 147. 184
  148. 148. Thanks! Q&A 185 https://jingdongwang2017.github.io/Projects/HRNet/

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