What question did this study set out to answer?

The aim is to improve knowledge-based visual question answering by addressing cross-modal semantic gaps.

April 30, 2026ElectronicsOpen Access

Bridging Cross-Modal Semantic Gaps with Multi-Source Semantic Anchors in Knowledge-Based Visual Question Answering

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Proposed framework enhances reasoning in KB-VQA by bridging semantic gaps, integrating multiple sources of information.

The aim is to improve knowledge-based visual question answering by addressing cross-modal semantic gaps.
Developed a unified framework using multi-source semantic anchors for aligning vision and textual features.
Implemented a cross-residual gating mechanism to reduce modality noise during cross-modal fusion.
Adopted contrastive learning to enhance cross-modal alignment and used a retrieve-then-read pipeline for answer generation.
Framework demonstrates improved performance on OK-VQA, FVQA, and A-OKVQA datasets.
Outperforms state-of-the-art methods across multiple evaluation metrics.

JunMing et al. (2026) studied this question.

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April 30, 2026ElectronicsOpen Access

View Full Paper

Ask AI

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Authors

Proposed framework enhances reasoning in KB-VQA by bridging semantic gaps, integrating multiple sources of information.

The aim is to improve knowledge-based visual question answering by addressing cross-modal semantic gaps.
Developed a unified framework using multi-source semantic anchors for aligning vision and textual features.
Implemented a cross-residual gating mechanism to reduce modality noise during cross-modal fusion.
Adopted contrastive learning to enhance cross-modal alignment and used a retrieve-then-read pipeline for answer generation.
Framework demonstrates improved performance on OK-VQA, FVQA, and A-OKVQA datasets.
Outperforms state-of-the-art methods across multiple evaluation metrics.

JunMing et al. (2026) studied this question.

View Full Paper

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