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

• AcceptedNov 29, 2019
• PublishedJan 16, 2020
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### Abstract

Semantic segmentation is a fundamental task in image analysis. The issue of semantic segmentation is to extract discriminative features for distinguishing different objects and recognizing hard examples. However, most existing methods have limitations on resolving this problem. To tackle this problem, we identify the contributions of the edge and saliency information for segmentation and present a novel end-to-end network, termed cross-guidance network (CGNet) to leverage them to benefit the semantic segmentation.The edge and saliency detection network are unified into the CGNet, and model the intrinsic information among them, guiding the process of extracting discriminative features. Specifically, the CGNet attempts to extract segmentation, edge, and salient features, simultaneously. Then it transfers them into the cross-guidance module (CGM) to generate the pre-knowledge features based on the modeled information, optimizing the context feature extraction process. The proposed approach is extensively evaluated on PASCAL VOC 2012, PASCAL-Person-Part, and Cityscapes, and achieves state-of-the-art performance, demonstrating the superiority of the proposed approach.

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• Figure 1

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

• 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.

• 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 95.9 39.3 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 75.2 94.0 _73.8 79.6 96.5 93.7 94.1 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] 46.8 71.8 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] 62.6 81.9 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 94.5 88.8 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] 72.4 89.1 _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] 87.15 72.28 _57.07 56.21 52.43 _50.36 97.72 67.60 DeepLab-v3+ [7] – – – – – – – 67.84 PGN [69] 90.89 75.12 55.83 64.61 55.42 41.57 95.33 _68.40 CGNet (ours) _87.69 _72.32 63.02 _63.62 _55.34 52.99 95.98 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

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