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-coherent features from self-supervised models to compute attention scores to mitigate mask inaccuracies. Regarding feature representation, we devise a refinement mechanism to simultaneously strengthen spatial details and semantic representation. Within the output space, we unify predictions via voting in each mask to enhance spatial consistency. To mitigate contextual bias and enhance prediction accuracy of objects, we further extend the framework by incorporating a cross-image object retrieval paradigm, which incurs minimal computational overhead. We build a memory bank of CorrCLIP-enhanced features from unlabeled images and implement a regime of multi-CLIP ensemble. By aggregating semantically consistent object features across diverse contexts retrieved from the memory bank, CorrCLIPv2 mitigates context-induced interference and obtains more robust predictions. Extensive experiments demonstrate that our approach establishes a new state-of-the-art on 10 standard benchmarks. Remarkably, it even surpasses fully supervised open-vocabulary methods across 22 multi-domain datasets. Code is available at: https://github.com/zdk258/CorrCLIP.