Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval

• URL: http://arxiv.org/abs/2506.10202v1
• Date: Wed, 11 Jun 2025 21:37:54 GMT
• Title: Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval
• Authors: Shubhashis Roy Dipta, Francis Ferraro,
• Abstract summary: Recent approaches have shown impressive proficiency in extracting and leveraging parametric knowledge from Large-Language Models (LLMs) and Vision-Language Models (VLMs)<n>We present Q2E: a Query-to-Event decomposition method for zero-shot multilingual text-to-video retrieval.
• Score: 9.230429417848393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent approaches have shown impressive proficiency in extracting and leveraging parametric knowledge from Large-Language Models (LLMs) and Vision-Language Models (VLMs). In this work, we consider how we can improve the identification and retrieval of videos related to complex real-world events by automatically extracting latent parametric knowledge about those events. We present Q2E: a Query-to-Event decomposition method for zero-shot multilingual text-to-video retrieval, adaptable across datasets, domains, LLMs, or VLMs. Our approach demonstrates that we can enhance the understanding of otherwise overly simplified human queries by decomposing the query using the knowledge embedded in LLMs and VLMs. We additionally show how to apply our approach to both visual and speech-based inputs. To combine this varied multimodal knowledge, we adopt entropy-based fusion scoring for zero-shot fusion. Through evaluations on two diverse datasets and multiple retrieval metrics, we demonstrate that Q2E outperforms several state-of-the-art baselines. Our evaluation also shows that integrating audio information can significantly improve text-to-video retrieval. We have released code and data for future research.

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