RA Loss: Relation-Aware Loss for Robust Person Re-identification

Kan Wang, Shuping Hu, Jun Cheng, Jun Cheng, Jianxin Pang, Huan Tan; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 177-194


Previous relation-based losses in person re-identification (ReI- D) typically comprise two sequential steps: they firstly sample both pos- itive pair and negative pair and then deploy constraints to simultane- ously improve intra-identity compactness and inter-identity separability. However, existing relation-based losses usually place emphasis on ex- ploring the relation between images and therefore consider only several pairs during each optimization. This inevitably leads to different con- vergence status for pairs of the same kind and brings about the intra- pair variance problem. Accordingly, we propose a novel Relation-Aware (RA) loss to address the intra-pair variance via exploring the informa- tive relation across pairs. In brief, we introduce a macro-constraint and a micro-constraint. The macro-constraint encourages the separation of positive pair and negative pair via pushing far apart the two "center- s" of the positive pair and the negative pair. The "center" of each kind of pair are obtained via averaging all the pairs of the same kind. The micro-constraint further enhances the compactness by minimizing the discrepancies among pairs of the same kind. The two constraints work cooperatively to relieve the intra-pair variance and improve the quali- ty of pedestriansar representation. Results of extensive experiments on three widely used ReID benchmarks, i.e., Market-1501, DukeMTMC- ReID and CUHK03, demonstrate that the RA loss brings improvements over existing relation-based losses.

Related Material

@InProceedings{Wang_2022_ACCV, author = {Wang, Kan and Hu, Shuping and Cheng, Jun and Cheng, Jun and Pang, Jianxin and Tan, Huan}, title = {RA Loss: Relation-Aware Loss for Robust Person Re-identification}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2022}, pages = {177-194} }