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The document presents a novel retrieval framework called Bidirectional Likelihood Estimation with Multi-Modal Large Language Models (BLiM) for Text-Video Retrieval, addressing the issue of candidate prior bias in existing models. BLiM improves retrieval performance by leveraging both query and candidate likelihoods, enabling deeper token-level interactions and reducing reliance on textual priors. The framework, equipped with a Candidate Prior Normalization (CPN) module, outperforms state-of-the-art models b
- Author
- BURMESE COBRA
- Language
- EN