ResearchResearch paperComputer Vision · Multimodal Models · Large Language Models1 source · Oct 17, 2026

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.
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.

[1]CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 03:53 PM
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.

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