CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding

• URL: http://arxiv.org/abs/2209.10918v2
• Date: Tue, 30 May 2023 02:03:34 GMT
• Title: CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
• Authors: Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Zheng Shou, Nan Duan
• Abstract summary: This paper tackles an emerging and challenging problem of long video temporal grounding(VTG) Compared with short videos, long videos are also highly demanded but less explored. We propose CONE, an efficient COarse-to-fiNE alignment framework.
• Score: 70.7882058229772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and weaker multi-modal alignment. To address these challenges, we propose CONE, an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play framework on top of existing VTG models to handle long videos through a sliding window mechanism. Specifically, CONE (1) introduces a query-guided window selection strategy to speed up inference, and (2) proposes a coarse-to-fine mechanism via a novel incorporation of contrastive learning to enhance multi-modal alignment for long videos. Extensive experiments on two large-scale long VTG benchmarks consistently show both substantial performance gains (e.g., from 3.13% to 6.87% on MAD) and state-of-the-art results. Analyses also reveal higher efficiency as the query-guided window selection mechanism accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. Codes have been released at https://github.com/houzhijian/CONE.

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