Papers › Listen to Look: Action Recognition by Previewing Audio

Listen to Look: Action Recognition by Previewing Audio

10 Dec 2019CVPR 2020 6arXiv:1912.04487archive 2025-07-28

Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, Lorenzo Torresani

In the face of the video data deluge, today's expensive clip-level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short-term and long-term visual redundancies. First, we devise an ImgAud2Vid framework that hallucinates clip-level features by distilling from lighter modalities---a single frame and its accompanying audio---reducing short-term temporal redundancy for efficient clip-level recognition. Second, building on ImgAud2Vid, we further propose ImgAud-Skimming, an attention-based long short-term memory network that iteratively selects useful moments in untrimmed videos, reducing long-term temporal redundancy for efficient video-level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state-of-the-art in terms of both recognition accuracy and speed.

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Code

facebookresearch/Listen-to-Look mentioned on GitHubpytorchCC-BY-4.0 report

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Tasks

Action Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition ActivityNet ListenToLook mAP 89.9 #8 of 16 Archive leaderboard report

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Methods

Memory Network

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