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arXiv · 2404.12216

ProTA: Probabilistic Token Aggregation for Text-Video Retrieval

Han Fang·Xianghao Zang·Chao Ban·Zerun Feng·Lanxiang Zhou·Zhongjiang He·Yongxiang Li·Hao Sun
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Abstract

Text-video retrieval aims to find the most relevant cross-modal samples for a given query. Recent methods focus on modeling the whole spatial-temporal relations. However, since video clips contain more diverse content than captions, the model aligning these asymmetric video-text pairs has a high risk of retrieving many false positive results. In this paper, we propose Probabilistic Token Aggregation (ProTA) to handle cross-modal interaction with content asymmetry. Specifically, we propose dual partial-related aggregation to disentangle and re-aggregate token representations in both low-dimension and high-dimension spaces. We propose token-based probabilistic alignment to generate token-level probabilistic representation and maintain the feature representation diversity. In addition, an adaptive contrastive loss is proposed to learn compact cross-modal distribution space. Based on extensive experiments, ProTA achieves significant improvements on MSR-VTT (50.9%), LSMDC (25.8%), and DiDeMo (47.2%).

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Han Fang, Xianghao Zang, Chao Ban, Zerun Feng, Lanxiang Zhou, Zhongjiang He, Yongxiang Li, Hao Sun. 2024-04-20. ProTA: Probabilistic Token Aggregation for Text-Video Retrieval. https://arxiv.org/abs/2404.12216

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