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arXiv · 2608.02907

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

Jingchen Sun·Shaobo Han·Ruiyi Zhang·Naresh Kumar Devulapally·Ming Liu·Yitao Long·Vishnu Suresh Lokhande·Changyou Chen
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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.

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Jingchen Sun, Shaobo Han, Ruiyi Zhang, Naresh Kumar Devulapally, Ming Liu, Yitao Long, Vishnu Suresh Lokhande, Changyou Chen. 2026-08-03. Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering. https://arxiv.org/abs/2608.02907

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