DiffCLIP: Differential Attention Meets CLIP

Hasan Abed Al Kader Hammoud, Bernard Ghanem

DiffCLIP: Differential Attention Meets CLIP: 5 upvotes on Hugging Face Daily Papers, #29 of 50 papers on 2025-03-11. Day-by-day upvote history.

We propose DiffCLIP, a novel vision-language model that extends the differential attention mechanism to CLIP architectures. Differential attention was originally developed for large language models to amplify relevant context while canceling out noisy information. In this work, we integrate this mechanism into CLIP's dual encoder (image and text) framework. With minimal additional parameters, DiffCLIP achieves superior performance on image-text understanding tasks. Across zero-shot classification, retrieval, and robustness benchmarks, DiffCLIP consistently outperforms baseline CLIP models. Notably, these gains come with negligible computational overhead, demonstrating that differential attention can significantly enhance multi-modal representations without sacrificing efficiency. Code can be found at https://github.com/hammoudhasan/DiffCLIP.

Paper page on Hugging Face · arXiv

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