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SCIENCE CHINA Information Sciences, Volume 63 , Issue 2 : 120104(2020) https://doi.org/10.1007/s11432-019-2718-7

CGNet: cross-guidance network for semantic segmentation

Zhijie ZHANG , Yanwei PANG *
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  • ReceivedJun 16, 2019
  • AcceptedNov 29, 2019
  • PublishedJan 16, 2020
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References

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  • Click to view Download high-quality image

    Figure 1

    (Color online) Examples of segmentation results, using different settings on whether to utilize edge (a) or salient object (b) information.

  • Click to view Download high-quality image

    Figure 2

    (Color online) Illustration of the proposed CGNet, which includes the main backbone network with a pyramid attentive module, a cross-guidance module (CGM), an edge detection head and a saliency detection head. `ResBlock' denotes the residual convolutional block in ResNet [14], while `$1\times1$', `$3\times3$',`$d$', `Up', and `Down' denote the convolutional layer with kernel size 1, convolutional layer with kernel size 3, dilated (atrous) rates of convolutional kernel, upsampling using non-parameterized bilinear interpolation, and downsampling, respectively. `CAM' and `SAM' refer to channel attentive module and spatial attentive module, respectively.

  • Click to view Download high-quality image

    Figure 3

    (Color online) Illustration of the proposed modules. `$1\times1$', `$3\times3$', `D-$3\times3$' and `DW-$1\times1$' denote the convolutional layer with kernel size 1, convolutional layer with kernel size 3, dilated convolutional layer [19]with kernel size 3, and depth-wise convolutional layer [16]with kernel size 1, respectively. (a) Channel attentive module; (b) spatial attentive module; (c) cross-guidance module.

  • Table 1   Segmentation results on the PASCAL VOC 2012 validation set$^{\rm~a)}$
    Method OS (training) OS (evaluating) pixAcc (%) mIoU (%)
    DeepLab-v2 [5] 16 16 94.21 75.60
    PSPNet [4] 16 16 94.62 76.82
    PAN [27] 16 16 95.03 78.37
    DeepLab-v3 [6] 16 16 – 77.21
    DeepLab-v3$^{\rm~b)}$ [6] 16 8 – 79.77
    DeepLab-v3+ [7] 16 16 – 78.85
    DeepLab-v3+$^{\rm~b)}$ [7] 16 16 – 80.22
    DeepLab-v3+$^{\rm~b)}$ [7] 16 8 – 80.57
    CGNet (ours) 16 16 95.32 79.89
    CGNet$^{\rm~b)}$ (ours) 16 16 95.67 81.04

    a

  • Table 2   Segmentation results on the PASCAL VOC 2012 test set w/o COCO pre-training$^{\rm~a)}$
    Method aero (%)bike (%)bird (%)boat (%)bottle (%)bus (%)car (%)cat (%)chair (%)cow (%)
    FCN [2] 76.8 34.2 68.9 49.4 60.3 75.3 74.7 77.6 21.4 62.5
    DeepLab-v2 [5] 84.4 54.5 81.5 63.6 65.9 85.1 79.1 83.4 30.7 74.1
    CRF-RNN [52] 87.5 39.0 79.7 64.2 68.3 87.6 80.8 84.4 30.4 78.2
    DeconvNet [18] 89.9 39.3 79.7 63.9 68.2 87.4 81.2 86.1 28.5 77.0
    DPN [53] 87.7 59.4 78.4 64.9 70.3 89.3 83.5 86.1 31.7 79.9
    Piecewise [54] 90.6 37.6 80.0 67.8 74.4 92.0 85.2 86.2 39.1 81.2
    AAF [55] 91.3 _72.9 90.7 68.2 77.7 95.6 90.7 94.7 _40.9 89.5
    ResNet38 [56] 94.4 _72.9 _94.9 68.8 78.4 90.6 90.0 92.1 40.1 90.4
    PSPNet [4] 91.8 71.9 94.7 71.2 75.8 95.2 89.9 95.939.3 90.7
    EncNet [21] 94.1 69.2 96.3 76.7 86.2_96.3 90.7 94.2 38.8 90.7
    PAN [27] 95.7 75.294.0 _73.8 79.6 96.5 93.794.1 40.5 93.3
    CGNet (ours) _95.3 72.6 94.6 71.8 _82.0 95.7 _91.9 _95.8 41.8_91.5
    Method table (%)dog(%)horse (%)mbike (%)person (%)plant (%)sheep (%)sofa (%)train (%)tv (%)mIoU (%)
    FCN [2] 46.871.8 63.9 76.5 73.9 45.2 72.4 37.4 70.9 55.1 62.2
    DeepLab-v2 [5] 59.8 79.0 76.1 83.2 80.8 59.7 82.2 50.4 73.1 63.7 71.6
    CRF-RNN [52] 60.4 80.5 77.8 83.1 80.6 59.5 82.8 47.8 78.3 67.1 72.0
    DeconvNet [18] 62.0 79.0 80.3 83.6 80.2 58.8 83.4 54.3 80.7 65.0 72.5
    DPN [53] 62.681.9 80.0 83.5 82.3 60.5 83.2 53.4 77.9 65.0 74.1
    Piecewise [54] 58.9 83.8 83.9 84.3 84.8 62.1 83.2 58.2 80.8 72.3 75.3
    AAF [55] 72.6 91.6_94.1 88.3 88.8 67.3 92.9 62.6 85.2 74.0 82.2
    ResNet38 [56] 71.7 89.9 93.7 _91.0 89.1 71.3 90.7 61.3 _87.7 78.1 82.5
    PSPNet [4] 71.7 90.5 94.588.8 89.6_72.8 89.6 _64.0 85.1 76.3 82.6
    EncNet [21] _73.3 90.0 92.5 88.8 87.9 68.7 92.6 59.0 86.4 73.4 82.9
    PAN [27] 72.489.1 _94.1 91.6_89.5 73.6_93.2 62.8 87.3 _78.6 _84.0
    CGNet (ours) 74.4_91.0 92.1 90.3 89.3 71.5 94.1 67.2 88.6 81.4 84.2

    a

  • Table 3   Segmentation results on the PASCAL-Person-Part test set$^{\rm~a)}$
    Method Head (%)Torso (%)U-Arm (%)L-Arm (%)U-Leg (%)L-Leg (%)B.G. (%)mIoU (%)
    HAZN [57] 80.79 59.11 43.05 42.76 38.99 34.46 93.59 56.11
    Attention [58] 81.47 59.06 44.15 42.50 38.28 35.62 93.65 56.39
    LG-LSTM [59] 82.72 60.99 45.40 47.76 42.33 37.96 88.63 57.97
    Attention+SSL [60] 83.26 62.40 47.80 45.58 42.32 39.48 94.68 59.36
    Attention+MMAN [61] 82.58 62.83 48.49 47.37 42.80 40.40 94.92 59.91
    Graph LSTM [62] 82.69 62.68 46.88 47.71 45.66 40.93 94.59 60.16
    SS-NAN [63] 86.43 67.28 51.09 48.07 44.82 42.15 _97.23 62.44
    Structure LSTM [64] 82.89 67.15 51.42 48.72 51.72 45.91 97.18 63.57
    Joint [49] 85.50 67.87 54.72 54.30 48.25 44.76 95.32 64.39
    DeepLab-v2 [5] – – – – – – – 64.94
    MuLA [65] – – – – – – – 65.10
    PCNet [66] 86.81 69.06 55.35 55.27 50.21 48.54 96.07 65.90
    Holistic [67] – – – – – – – 66.30
    WSHP [68] 87.1572.28_57.0756.2152.43_50.36 97.7267.60
    DeepLab-v3+ [7] – – – – – – – 67.84
    PGN [69] 90.89 75.1255.83 64.61 55.4241.57 95.33 _68.40
    CGNet (ours) _87.69 _72.32 63.02_63.62 _55.34 52.9995.98 70.14

    a

  • Table 4   Segmentation results on the Cityscapes test set$^{\rm~a)}$
    Method IoU cla. (%)iIoU cla. (%)IoU cat. (%)iIoU cat. (%)
    FCN [2] 65.3 41.7 85.7 70.1
    DeepLab-v2 [5] 70.4 42.6 86.4 67.7
    RefineNet [20] 73.6 – – –
    DSSPN [70] 76.6 56.2 89.6 77.8
    GCN [28] 76.9 – – –
    DUC [71] 77.6 53.6 90.1 75.2
    SAC [72] 78.1 55.2 90.6 78.3
    PSPNet [4] 78.4 56.7 90.6 78.6
    BiSeNet [73] 78.9 – – –
    AAF [55] 79.1 56.1 90.8 78.5
    DFN [74] 79.3 – – –
    PSANet [75] 80.1 – – –
    ANN [76] 81.3 – – –
    DANet [44] 81.5 – – –
    CGNet (ours) 81.3 62.5 91.4 79.7

    a

  • Table 5   Ablation study on the PASCAL-Person-Part test set$^{\rm~a)}$
    Method pixAcc (%) mIoU (%)
    DeepLab-v2 [5] 93.55 64.94
    DeepLab-v3+ [7] 94.23 67.84
    Base 93.02 62.62
    Base + Pyramid Attention 94.02 66.95
    Base + Pyramid Attention + Edge 94.21 67.78
    Base + Pyramid Attention + Salient 94.17 67.63
    Base + Pyramid Attention + Edge + Salient 94.33 68.17
    textcolorblack
    Base + Pyramid Attention + Concat (edge & salient) 94.44 68.46
    Base + Pyramid + CGM 94.78 70.14

    a