Repurposing CLIP to Localize at Pixel Level

Jiaxiang Fang; Shiqiang Ma; Jing Wang; Siyu Chen; Fei Guo; Shengfeng He

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arXiv · 2607.05253

Repurposing CLIP to Localize at Pixel Level

Jiaxiang Fang·Shiqiang Ma·Jing Wang·Siyu Chen·Fei Guo·Shengfeng He

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Abstract

Large-scale Vision-Language Models like CLIP have demonstrated impressive open-set localization capabilities at the image level. However, adapting this capability to pixel-level dense prediction poses challenges due to global feature biases. In this paper, we introduce CLIPix, a simple yet effective framework that repurposes CLIP to perform pixel-level localization. By tracing back CLIP's classification process, CLIPix identifies object-specific attentive regions and repurposes them as pixel-level localization cues. To address noise introduced by global biases, we propose a Noise-Resistant Correction strategy, refining these cues for more precise segmentation. Additionally, we introduce a Localization Embedding strategy to integrate both localization and enriched detail information, enabling accurate, high-resolution segmentation. Our approach preserves CLIP's generalization strength and unlocks its potential for segmenting arbitrary objects. Extensive experiments on the PASCAL and COCO datasets demonstrate that CLIPix achieves state-of-the-art performance, underscoring its effectiveness.

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BibTeX RIS

Jiaxiang Fang, Shiqiang Ma, Jing Wang, Siyu Chen, Fei Guo, Shengfeng He. 2026-07-07. Repurposing CLIP to Localize at Pixel Level. https://arxiv.org/abs/2607.05253

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