  Multi-label recognition (MLR) with incomplete labels is very challenging.
Recent works strive to explore the image-to-label correspondence in the
vision-language model, \ie, CLIP, to compensate for insufficient annotations.
In spite of promising performance, they generally overlook the valuable prior
about the label-to-label correspondence. In this paper, we advocate remedying
the deficiency of label supervision for the MLR with incomplete labels by
deriving a structured semantic prior about the label-to-label correspondence
via a semantic prior prompter. We then present a novel Semantic Correspondence
Prompt Network (SCPNet), which can thoroughly explore the structured semantic
prior. A Prior-Enhanced Self-Supervised Learning method is further introduced
to enhance the use of the prior. Comprehensive experiments and analyses on
several widely used benchmark datasets show that our method significantly
outperforms existing methods on all datasets, well demonstrating the
effectiveness and the superiority of our method. Our code will be available at
https://github.com/jameslahm/SCPNet.
