$β$-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917301609037824 |
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| author | Zohra, Fatimah Zhao, Chen Itani, Hani Ghanem, Bernard |
| author_facet | Zohra, Fatimah Zhao, Chen Itani, Hani Ghanem, Bernard |
| contents | CLIP achieves strong zero-shot image-text retrieval by aligning global vision and text representations, yet it falls behind on fine-grained tasks even when fine-tuned on long, detailed captions. In this work, we propose $β$-CLIP, a multi-granular text-conditioned contrastive learning framework designed to achieve hierarchical alignment between multiple textual granularities-from full captions to sentences and phrases-and their corresponding visual regions. For each level of granularity, $β$-CLIP utilizes cross-attention to dynamically pool image patches, producing contextualized visual embeddings. To address the semantic overlap inherent in this hierarchy, we introduce the $β$-Contextualized Contrastive Alignment Loss ($β$-CAL). This objective parameterizes the trade-off between strict query-specific matching and relaxed intra-image contextualization, supporting both soft Cross-Entropy and hard Binary Cross-Entropy formulations. We find that each loss interacts differently with hierarchical supervision: CE's softmax sharpens fine-grained discrimination, while BCE's sigmoid favors long-text retrieval while both benefit from hierarchy. Through extensive experiments, we demonstrate that $β$-CLIP significantly improves dense alignment: achieving 91.8% T2I 92.3% I2T at R@1 on Urban1K and 30.9% on FG-OVD (Hard), setting state-of-the-art among methods trained without hard negatives. $β$-CLIP establishes a robust, adaptive baseline for dense vision-language correspondence. The code and models are released at https://github.com/fzohra/B-CLIP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12678 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $β$-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment Zohra, Fatimah Zhao, Chen Itani, Hani Ghanem, Bernard Computer Vision and Pattern Recognition CLIP achieves strong zero-shot image-text retrieval by aligning global vision and text representations, yet it falls behind on fine-grained tasks even when fine-tuned on long, detailed captions. In this work, we propose $β$-CLIP, a multi-granular text-conditioned contrastive learning framework designed to achieve hierarchical alignment between multiple textual granularities-from full captions to sentences and phrases-and their corresponding visual regions. For each level of granularity, $β$-CLIP utilizes cross-attention to dynamically pool image patches, producing contextualized visual embeddings. To address the semantic overlap inherent in this hierarchy, we introduce the $β$-Contextualized Contrastive Alignment Loss ($β$-CAL). This objective parameterizes the trade-off between strict query-specific matching and relaxed intra-image contextualization, supporting both soft Cross-Entropy and hard Binary Cross-Entropy formulations. We find that each loss interacts differently with hierarchical supervision: CE's softmax sharpens fine-grained discrimination, while BCE's sigmoid favors long-text retrieval while both benefit from hierarchy. Through extensive experiments, we demonstrate that $β$-CLIP significantly improves dense alignment: achieving 91.8% T2I 92.3% I2T at R@1 on Urban1K and 30.9% on FG-OVD (Hard), setting state-of-the-art among methods trained without hard negatives. $β$-CLIP establishes a robust, adaptive baseline for dense vision-language correspondence. The code and models are released at https://github.com/fzohra/B-CLIP. |
| title | $β$-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.12678 |