LocVTP: Video-Text Pre-training for Temporal Localization

• URL: http://arxiv.org/abs/2207.10362v1
• Date: Thu, 21 Jul 2022 08:43:51 GMT
• Title: LocVTP: Video-Text Pre-training for Temporal Localization
• Authors: Meng Cao, Tianyu Yang, Junwu Weng, Can Zhang, Jue Wang, and Yuexian Zou
• Abstract summary: Video-Text Pre-training aims to learn transferable representations for various downstream tasks from large-scale web videos. In this paper, we experimentally analyze and demonstrate the incompatibility of current VTP methods with localization tasks. We propose a novel localization-oriented Video-Text Pre-training framework, dubbed as LocVTP.
• Score: 71.74284893790092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-Text Pre-training (VTP) aims to learn transferable representations for various downstream tasks from large-scale web videos. To date, almost all existing VTP methods are limited to retrieval-based downstream tasks, e.g., video retrieval, whereas their transfer potentials on localization-based tasks, e.g., temporal grounding, are under-explored. In this paper, we experimentally analyze and demonstrate the incompatibility of current VTP methods with localization tasks, and propose a novel Localization-oriented Video-Text Pre-training framework, dubbed as LocVTP. Specifically, we perform the fine-grained contrastive alignment as a complement to the coarse-grained one by a clip-word correspondence discovery scheme. To further enhance the temporal reasoning ability of the learned feature, we propose a context projection head and a temporal aware contrastive loss to perceive the contextual relationships. Extensive experiments on four downstream tasks across six datasets demonstrate that our LocVTP achieves state-of-the-art performance on both retrieval-based and localization-based tasks. Furthermore, we conduct comprehensive ablation studies and thorough analyses to explore the optimum model designs and training strategies.

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