Papers › Learning from Video and Text via Large-Scale Discriminative Clustering

Learning from Video and Text via Large-Scale Discriminative Clustering

27 Jul 2017ICCV 2017 10arXiv:1707.09074archive 2025-07-28

Antoine Miech, Jean-Baptiste Alayrac, Piotr Bojanowski, Ivan Laptev, Josef Sivic

Discriminative clustering has been successfully applied to a number of weakly-supervised learning tasks. Such applications include person and action recognition, text-to-video alignment, object co-segmentation and colocalization in videos and images. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We apply the proposed method to the problem of weakly supervised learning of actions and actors from movies together with corresponding movie scripts. The scaling up of the learning problem to 66 feature length movies enables us to significantly improve weakly supervised action recognition.

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jpeyre/unrel mentioned on GitHub report

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Tasks

Action RecognitionClusteringTemporal Action LocalizationVideo AlignmentVideo RetrievalWeakly-Supervised Action RecognitionWeakly-supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Retrieval LSMDC Large-Scale Discriminative Clustering text-to-video Median Rank 52 #35 of 38 Archive leaderboard report
Video Retrieval LSMDC Large-Scale Discriminative Clustering text-to-video R@1 7.3 #35 of 38 Archive leaderboard report
Video Retrieval LSMDC Large-Scale Discriminative Clustering text-to-video R@10 27.1 #35 of 38 Archive leaderboard report
Video Retrieval LSMDC Large-Scale Discriminative Clustering text-to-video R@5 19.2 #35 of 38 Archive leaderboard report

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