Cross-Domain Spatial Co-Attention Network for Sketch-Based Image Retrieval

Lingzhi Yu; Xifan Zhang

doi:10.3788/LOP202259.2215009

Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2215009(2022)

Cross-Domain Spatial Co-Attention Network for Sketch-Based Image Retrieval

Lingzhi Yu^* and Xifan Zhang

Author Affiliations

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

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Figures & Tables(5)

Fig. 1. Network architecture of the proposed method

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Fig. 2. Some retrieval examples of proposed method on Sketchy dataset

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Table 1. mAP of different methods on Sketchy and TU-Berlin datasets

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Table 1. mAP of different methods on Sketchy and TU-Berlin datasets

Method	Sketchy	TU-Berlin
3D shape^［21］	0.084	0.054
HOG^［22］	0.115	0.091
GF-HOG^［6］	0.157	0.119
SHELO^［23］	0.161	0.123
LKS^［24］	0.190	0.157
SaN^［7］	0.208	0.154
Siamese CNN^［25］	0.481	0.322
Siamese-AlexNet^［18］	0.518	0.367
GN Triplet^［17］	0.529	0.187
Triplet-AlexNet^［18］	0.573	0.448
DSH^［18］	0.783	0.570
GDH^［10］	0.810	0.690
Semi3-Net^［11］	0.916	0.800
Proposed method（discrete）	0.932	0.797
Proposed method（continuous）	0.933	0.799

Table 2. Ablation study on Sketchy dataset
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Table 2. Ablation study on Sketchy dataset
Method mAP
w/o edge map branch 0.899
w/o spatial co-attention 0.923
w/o intra-class loss 0.928
w/o quantization loss 0.931
Full model 0.933

Table 3. Evaluation of auxiliary classifier on Sketchy dataset
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Table 3. Evaluation of auxiliary classifier on Sketchy dataset
Input of auxiliary classifier Precision mAP
High-level feature 0.996 0.911
Code 0.999 0.933

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Lingzhi Yu, Xifan Zhang. Cross-Domain Spatial Co-Attention Network for Sketch-Based Image Retrieval[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215009

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