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Recent success of Contrastive Language-Image Pre-training~(CLIP) has showngreat promise in pixel-level open-vocabulary learning tasks. A general paradigmutilizes CLIP's text and patch embeddings to generate semantic masks. However,existing models easily misidentify input pixels from unseen classes, thusconfusing novel classes with semantically-similar ones. In our work, wedisentangle the ill-posed optimization problem into two parallel processes: oneperforms semantic matching individually, and the other judges reliability forimproving discrimination ability. Motivated by special tokens in languagemodeling that represents sentence-level embeddings, we design a trusty tokenthat decouples the known and novel category prediction tendency. With almost noextra overhead, we upgrade the pixel-level generalization capacity of existingmodels effectively. Our TagCLIP (CLIP adapting with Trusty-guidance) boosts theIoU of unseen classes by 7.4% and 1.7% on PASCAL VOC 2012 and COCO-Stuff 164K.

TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation