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arxiv: 2209.10918 · v2 · pith:557OFCM4 · submitted 2022-09-22 · cs.CV · cs.CL· cs.IR

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
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Nan Duan
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Reviewed by Pithpith:557OFCM4open to challenge →

classification cs.CV cs.CLcs.IR
keywords longconealignmentvideoscoarse-to-fineframeworkinferencemechanism
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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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Cited by 4 Pith papers

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    UniversalVTG is a lightweight foundation model for video temporal grounding that achieves state-of-the-art results across five benchmarks while being over 100 times smaller than recent MLLM-based methods.

  4. Multi-Scale Contrastive Learning for Video Temporal Grounding

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    A multi-scale and cross-scale contrastive learning framework uses intra-encoder stage features and a new sampling process to link short-range and long-range video moments for temporal grounding.