Papers › Temporal Context Network for Activity Localization in Videos

Temporal Context Network for Activity Localization in Videos

8 Aug 2017ICCV 2017 10arXiv:1708.02349archive 2025-07-28

Xiyang Dai, Bharat Singh, Guyue Zhang, Larry S. Davis, Yan Qiu Chen

We present a Temporal Context Network (TCN) for precise temporal localization of human activities. Similar to the Faster-RCNN architecture, proposals are placed at equal intervals in a video which span multiple temporal scales. We propose a novel representation for ranking these proposals. Since pooling features only inside a segment is not sufficient to predict activity boundaries, we construct a representation which explicitly captures context around a proposal for ranking it. For each temporal segment inside a proposal, features are uniformly sampled at a pair of scales and are input to a temporal convolutional neural network for classification. After ranking proposals, non-maximum suppression is applied and classification is performed to obtain final detections. TCN outperforms state-of-the-art methods on the ActivityNet dataset and the THUMOS14 dataset.

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Tasks

General ClassificationTemporal Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition THUMOS’14 Dai et. al. mAP@0.4 33.3 #7 of 10 Archive leaderboard report
Action Recognition THUMOS’14 Dai et. al. mAP@0.5 25.6 #7 of 10 Archive leaderboard report

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