CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation aims to classify pixels without being restricted to a fixed set of categories. Although Contrastive Language-Image Pre-training (CLIP) demonstrates remarkable potential for open-vocabulary recognition, its segmentation capability is impaired by noisy inter-class patch interactions. To this end, this work proposes a unified training-free object-centric framework, named CorrCLIPv2, from three complementary aspects of patch correlation, feature representation, and output regularization. Specifically, for patch correlation, we leverage class-agnostic mask generators to explicitly disentangle inter-class interactions. Moreover, we utilize spatially-coheren...
| Authors | Dengke Zhang, Fagui Liu, Quan Tang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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