Abstract
How to learn pixel-level representations of human parts without supervision is a challenging task. However, despite its significance, a few works explore this challenge. In this work, we propose a contrastive and consistent learning network (\(C^{2}L\)) for unsupervised human parsing. \(C^{2}L\) mainly consists of a part contrastive module and a pixel consistent module. We design a part contrastive module to distinguish the same semantic human parts from other ones by contrastive learning, which pulls the same semantic parts closer and pushes different semantic ones away. A pixel consistent module is proposed to obtain spatial correspondence in each view of images, which can select semantic-relevant image pixels and suppress semantic-irrelevant ones. To improve the pattern analysis ability, we perform a sparse operation on the feed-forward networks of the pixel consistent module. Extensive experiments on the popular human parsing benchmark show that our method achieves competitive performance.
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Acknowledgement
This work was supported in part by the National Key Research & Development Program (No. 2020YFC2003901), Chinese National Natural Science Foundation Projects (No. 62206280, 62176256, 61876178, 61976229 and 62106264), the Youth Innovation Promotion Association CAS (No. Y2021131).
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Zhang, X. et al. (2022). Contrastive and Consistent Learning for Unsupervised Human Parsing. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_23
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