TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
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Recent success of Contrastive Language-Image Pre-training (CLIP) has shown great promise in pixel-level open-vocabulary learning tasks. A general paradigm utilizes CLIP's text and patch embeddings to generate semantic masks. However, existing models easily misidentify input pixels from unseen classes, thus confusing novel classes with semantically-similar ones. In our work, we disentangle the ill-posed optimization problem into two parallel processes: one performs semantic matching individually, and the other judges reliability for improving discrimination ability. Motivated by special tokens in language modeling that represents sentence-level embeddings, we design a trusty token that decouples the known and novel category prediction tendency. With almost no extra overhead, we upgrade the pixel-level generalization capacity of existing models effectively. Our TagCLIP (CLIP adapting with Trusty-guidance) boosts the IoU of unseen classes by 7.4
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