Temporal Perceiving Video-Language Pre-training

• URL: http://arxiv.org/abs/2301.07463v1
• Date: Wed, 18 Jan 2023 12:15:47 GMT
• Title: Temporal Perceiving Video-Language Pre-training
• Authors: Fan Ma, Xiaojie Jin, Heng Wang, Jingjia Huang, Linchao Zhu, Jiashi Feng, Yi Yang
• Abstract summary: This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description. Our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality.
• Score: 112.1790287726804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve global feature alignment across modalities. However, the local associations between videos and texts are not modeled, restricting the pre-training models' generality, especially for tasks requiring the temporal video boundary for certain query texts. This work introduces a novel text-video localization pre-text task to enable fine-grained temporal and semantic alignment such that the trained model can accurately perceive temporal boundaries in videos given the text description. Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features. To produce temporal boundaries, frame features in several videos are manually merged into a long video sequence that interacts with a text sequence. With the localization task, our method connects the fine-grained frame representations with the word representations and implicitly distinguishes representations of different instances in the single modality. Notably, comprehensive experimental results show that our method significantly improves the state-of-the-art performance on various benchmarks, covering text-to-video retrieval, video question answering, video captioning, temporal action localization and temporal moment retrieval. The code will be released soon.

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