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Multi-label recognition (MLR) with incomplete labels is very challenging.Recent works strive to explore the image-to-label correspondence in thevision-language model, \ie, CLIP, to compensate for insufficient annotations.In spite of promising performance, they generally overlook the valuable priorabout the label-to-label correspondence. In this paper, we advocate remedyingthe deficiency of label supervision for the MLR with incomplete labels byderiving a structured semantic prior about the label-to-label correspondencevia a semantic prior prompter. We then present a novel Semantic CorrespondencePrompt Network (SCPNet), which can thoroughly explore the structured semanticprior. A Prior-Enhanced Self-Supervised Learning method is further introducedto enhance the use of the prior. Comprehensive experiments and analyses onseveral widely used benchmark datasets show that our method significantlyoutperforms existing methods on all datasets, well demonstrating theeffectiveness and the superiority of our method. Our code will be available athttps://github.com/jameslahm/SCPNet.

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels