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arXiv · 2606.18553

Hierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning

Minh-Loi Nguyen·Xuan-Vu Le·Long-Bao Nguyen·Hoang-Bach Ngo·Trung-Nghia Le
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Abstract

Traditional image captioning methods often struggle to generate comprehensive, context-rich descriptions, especially for details not directly observable from visual cues. To overcome this, we propose a novel retrieval-augmented image captioning framework that generates captions with deeper insights, such as object attributes, event context, and underlying significance, by leveraging external knowledge. Our approach features a hierarchical multi-modal article retrieval mechanism that moves beyond monolithic text entities. This retrieval considers article structure-aware features, including weighted textual components (e.g., headlines, body sections) and visual placement patterns, alongside multi-faceted similarity computations (content--visual, visual--visual, and discourse positioning). A subsequent contextual relevance refinement stage further enhances the retrieved information. The retrieved articles then serve as the knowledge base for caption generation: first, a VLM generates a concise image description; second, we segment relevant information from the retrieved articles based on this description; and finally, an LLM utilizes both the description and extracted knowledge to generate a comprehensive, contextually detailed caption. We participated in the ACM Multimedia EVENTA 2025 Challenge and achieved 5th place with an overall score of 0.2824 on the private test set of the OpenEvent-V1 dataset. Source code is publicly released at https://github.com/mf0212/EVENTA-Challange.

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Minh-Loi Nguyen, Xuan-Vu Le, Long-Bao Nguyen, Hoang-Bach Ngo, Trung-Nghia Le. 2026-06-17. Hierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning. https://arxiv.org/abs/2606.18553

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