Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering

Abstract:Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. However, existing contrastive training methods typically treat all unmatched query-document pairs as equally informative negatives, which is problematic because many unmatched documents may still be semantically relevant or partially useful. We propose Bayesian Data Reweighting, a probabilistic framework that models query-document importance as latent variables and adaptively infers posterior weights to downweight likely false negatives. With closed-form posterior updates under conjugate priors and stochastic EM optimization, our method consistently improves retrieval accuracy across three retrievers and seven knowledge-based VQA benchmarks.

Machine Learning (cs.LG)

arXiv:2608.02907 [cs.LG]

(or arXiv:2608.02907v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.02907 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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