HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025

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Tai Nguyen, Vo Ngoc Minh Anh, Duc Dat Pham, Tran Quang Vinh, Nhu Duong Thi Quynh, Le Anh Tien, Tan Duy Le, Binh T. Nguyen

Published: 2025, Last Modified: 19 Mar 2026MMM (5) 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In the dynamic field of video retrieval, precise and effective search methods are crucial for managing complex datasets. We present HORUS, a novel approach based on multimodal Large Language Models (mLLMs) that advances video retrieval capabilities through two key innovations: (1) advanced multi-modal feature aggregation, integrating text-to-image search with CLIP, free-text search from captions generated by Video-LLaMA2, and visual features from Video-LLaMA to capture temporal dynamics; and (2) GPT-based query expansion, combined with an advanced filter, addresses issues with low-quality open-ended text queries and refines item searches based on type and location within a scene. This work provides cutting-edge solutions for the VBS 2025 challenge and offers valuable insights into enhancing video search techniques.