Not Just What's There: Enabling CLIP to Comprehend Negated Visual Descriptions Without Fine-tuning

• URL: http://arxiv.org/abs/2602.21035v1
• Date: Tue, 24 Feb 2026 15:55:39 GMT
• Title: Not Just What's There: Enabling CLIP to Comprehend Negated Visual Descriptions Without Fine-tuning
• Authors: Junhao Xiao, Zhiyu Wu, Hao Lin, Yi Chen, Yahui Liu, Xiaoran Zhao, Zixu Wang, Zejiang He,
• Abstract summary: Vision-Language Models (VLMs) like CLIP struggle to understand negation.<n>Existing methods refine negation understanding via fine-tuning CLIP's text encoder, risking overfitting.<n>We propose CLIPGlasses, a plug-and-play framework that enhances CLIP's ability to comprehend negated visual descriptions.
• Score: 23.10421006625293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) like CLIP struggle to understand negation, often embedding affirmatives and negatives similarly (e.g., matching "no dog" with dog images). Existing methods refine negation understanding via fine-tuning CLIP's text encoder, risking overfitting. In this work, we propose CLIPGlasses, a plug-and-play framework that enhances CLIP's ability to comprehend negated visual descriptions. CLIPGlasses adopts a dual-stage design: a Lens module disentangles negated semantics from text embeddings, and a Frame module predicts context-aware repulsion strength, which is integrated into a modified similarity computation to penalize alignment with negated semantics, thereby reducing false positive matches. Experiments show that CLIP equipped with CLIPGlasses achieves competitive in-domain performance and outperforms state-of-the-art methods in cross-domain generalization. Its superiority is especially evident under low-resource conditions, indicating stronger robustness across domains.

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