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arXiv · 2408.07249

Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach

Zechen Bai·Tianjun Xiao·Tong He·Pichao Wang·Zheng Zhang·Thomas Brox·Mike Zheng Shou
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Abstract

As online video content rapidly grows, the task of text-video retrieval (TVR) becomes increasingly important. A key challenge in TVR is the information asymmetry between video and text: videos are inherently richer in information, while their textual descriptions often capture only fragments of this complexity. This paper introduces a novel, data-centric framework to bridge this gap by enriching textual representations to better match the richness of video content. During training, videos are segmented into event-level clips and captioned to ensure comprehensive coverage. During retrieval, a large language model (LLM) generates semantically diverse queries to capture a broader range of possible matches. To enhance retrieval efficiency, we propose a query selection mechanism that identifies the most relevant and diverse queries, reducing computational cost while improving accuracy. Our method achieves state-of-the-art results across multiple benchmarks, demonstrating the power of data-centric approaches in addressing information asymmetry in TVR. This work paves the way for new research focused on leveraging data to improve cross-modal retrieval.

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Zechen Bai, Tianjun Xiao, Tong He, Pichao Wang, Zheng Zhang, Thomas Brox, Mike Zheng Shou. 2025-03-08. Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach. https://arxiv.org/abs/2408.07249

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