Enabling Collaborative Parametric Knowledge Calibration for Retrieval-Augmented Vision Question Answering

• URL: http://arxiv.org/abs/2504.04065v2
• Date: Tue, 01 Jul 2025 02:37:16 GMT
• Title: Enabling Collaborative Parametric Knowledge Calibration for Retrieval-Augmented Vision Question Answering
• Authors: Jiaqi Deng, Kaize Shi, Zonghan Wu, Huan Huo, Dingxian Wang, Guandong Xu,
• Abstract summary: Vision Question Answering (KB-VQA) systems address complex visual-grounded questions with knowledge retrieved from external knowledge bases.<n>We propose a unified retrieval-augmented VQA framework with collaborative parametric knowledge calibration.<n>Our approach achieves competitive performance against state-of-the-art models, delivering a significant 4.7% improvement in answering accuracy.
• Score: 16.14877145354785
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowledge-based Vision Question Answering (KB-VQA) systems address complex visual-grounded questions with knowledge retrieved from external knowledge bases. The tasks of knowledge retrieval and answer generation tasks both necessitate precise multimodal understanding of question context and external knowledge. However, existing methods treat these two stages as separate modules with limited interaction during training, which hinders bi-directional parametric knowledge sharing, ultimately leading to suboptimal performance. To fully exploit the cross-task synergy in KB-VQA, we propose a unified retrieval-augmented VQA framework with collaborative parametric knowledge calibration. The proposed framework can effectively adapt general multimodal pre-trained models for fine-grained, knowledge-intensive tasks while enabling the retriever and generator to collaboratively enhance and share their parametric knowledge during both training and inference. To enhance fine-grained understanding of questions and external documents, we also integrate late interaction mechanism into the proposed training framework. Additionally, we introduce a reflective-answering mechanism that allows the model to explicitly evaluate and refine its knowledge boundary. Our approach achieves competitive performance against state-of-the-art models, delivering a significant 4.7\% improvement in answering accuracy, and brings an average 7.5\% boost in base MLLMs' VQA performance.

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