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arXiv · 2209.10918

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

Zhijian Hou·Wanjun Zhong·Lei Ji·Difei Gao·Kun Yan·Wing-Kwong Chan·Chong-Wah Ngo·Zheng Shou·Nan Duan
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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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Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Zheng Shou, Nan Duan. 2022-09-22. CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding. https://arxiv.org/abs/2209.10918

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