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Zi-Dan Wang

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#artificial intelligence Preprint Oct 2026

Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder

CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a lightweight route to refine CLIP, but recent work argues that the standard contrastive loss is unsuitable for post-training due to catastrophic forgetting under small batches, mo...

Zi-Dan Wang, Ya-Qian Li, Xiao-Kai Zhang et al. · 0 citations

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