Grouped Double Attention Network for Semantic Segmentation

Table 1. Influence of the number of GPAM groups on network performance
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Table 1. Influence of the number of GPAM groups on network performance
Method Backbone PAM GNP mIoU /%
Baseline1 ResNet50 69.8
Baseline2 ResNet50 √ 83.2
Our1 ResNet50 1 84.1
Our2 ResNet50 2 84.9
Our3 ResNet50 4 84.4
Our4 ResNet50 8 84.2
Our5 ResNet50 16 82.9
Our6 ResNet50 64 81.1

Table 2. Influence of the number of GPAM basis sets on network performance
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Table 2. Influence of the number of GPAM basis sets on network performance
Method Backbone PAM NBP mIoU /%
Baseline2 ResNet50 √ 83.2
Our7 ResNet50 32 85.0
Our8 ResNet50 16 85.0
Our2 ResNet50 8 84.9

Table 3. Influence of the number of GCAM groups on network performance
View table
Table 3. Influence of the number of GCAM groups on network performance
Method Backbone CAM GNC mIoU /%
Baseline3 ResNet50 √ 82.6
Our-1 ResNet50 8 83.9
Our-2 ResNet50 16 84.1
Our-3 ResNet50 32 84.9

Table 4. Memory occupied by CAM and GCAM
View table
Table 4. Memory occupied by CAM and GCAM
Method GNC Memory /G mIoU /%
CAM -- 1.00 82.6
GCAM 8 0.85 83.9
GCAM 16 0.73 84.1
GCAM 32 0.68 84.9

Table 5. Influence of the size of the GCAM pooling on segmentation performance
View table
Table 5. Influence of the size of the GCAM pooling on segmentation performance
Method Backbone CAM PSC mIoU /%
Baseline3 ResNet50 √ 82.6
Our-4 ResNet50 4 84.7
Our-3 ResNet50 8 84.9
Our-5 ResNet50 16 84.3

Table 6. Experimental results of grouped double attention network and Baseline
View table
Table 6. Experimental results of grouped double attention network and Baseline
Method PAM CAM GPAM GCAM mIoU /%
Baseline1 69.8
Baseline2 √ 83.2
Baseline3 √ 82.6
Our7 √ 85.0
Our-3 √ 84.9
GDANet √ √ 85.6

Table 7. Experimental results of different methods in the PASCAL VOC2012 validation set unit: %

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Table 7. Experimental results of different methods in the PASCAL VOC2012 validation set unit: %

Method	FCN	DeepLabv2	DPN^[25]	DeepLabv3	PSP	DANet	Ours
Aero	82.4	84.4	87.7	88.0	87.4	90.1	92.8
Bike	47.4	54.5	59.4	56.3	56.3	61.8	67.8
Bird	81.2	81.5	78.4	86.3	85.7	91.7	91.8
Boat	68.6	63.6	64.9	69.4	79.4	75.6	82.5
Bottle	75.3	65.9	70.3	72.2	73.8	75.6	76.7
Bus	81.3	85.1	89.3	90.3	92.3	93.1	95.0
Car	79.9	79.1	83.5	85.7	87.3	88.5	90.7
Cat	81.6	83.4	86.1	89.6	92.3	92.9	92.7
Chair	33.7	30.7	31.7	28.9	53.3	53.4	61.7
Cow	68.4	74.1	79.9	85.9	90.4	93.3	94.8
Table	52.3	59.8	62.6	59.3	75.2	74.3	81.3
Dog	76.4	79	81.9	84.2	87.3	92	93.5
Horse	64.9	76.1	80	80.2	85.9	89.1	92.4
Mbike	73.4	83.2	83.5	84.2	83.8	85.4	88.7
Person	81.2	80.8	82.3	82.8	84.5	85.7	88.3
Plant	56.7	59.7	60.5	56.0	68.1	62.8	70.0
Sheep	69.7	82.2	83.2	78.5	87	91.6	92.6
Sofa	50.9	50.4	53.4	51.6	73	74.6	78.1
Train	78.5	73.1	77.9	84.5	91.1	90.2	92.0
Tv	70.1	63.7	65.0	69.6	71.5	73.1	77.1
mIoU	69.8	71.6	74.1	75.1	80.9	82.4	85.6

Table 8. Experimental results of different methods on the Cityscapes validation set unit: %

View table

Table 8. Experimental results of different methods on the Cityscapes validation set unit: %

Method	FCN	PSP	DANet	Ours	Method	FCN	PSP	DANet	Ours
Road	95.1	96.4	97.2	97.5	Sky	91.4	92	92.4	92.8
Sidewalk	67.8	74.4	77.8	79.3	Person	68.8	70.4	71.9	72.9
Building	88.5	89.1	89.8	90.1	Rider	47.9	49.9	52.2	53.3
Wall	50.5	52.9	56.1	57.1	Car	90.3	91.4	92.4	92.4
Fence	44.6	47.9	48.6	51.2	Truck	73.8	73.9	82.8	79.2
Pole	35.6	39.9	40.8	43.4	Bus	73.6	75.8	79.4	81.9
Traffic light	47.0	51.9	53.0	53.5	Train	62.8	66.4	70.8	74.5
Traffic sign	60.4	62.4	65.2	66.4	Motocycle	51.7	55.0	58.9	58.7
Vegetation	88.6	89.4	89.7	89.9	Bicycle	63.1	63.6	65.8	66.7
Terrain	55.6	57.6	60.7	60.9
mIoU	66.2	68.4	70.8	71.7	mIoU	66.2	68.4	70.8	71.7

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Xiaolong Chen, Ji Zhao, Siyi Chen, Xinhao Du, Xin Liu. Grouped Double Attention Network for Semantic Segmentation[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210007

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