Papers › Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
Tianwei Lin, Xu Zhao, Zheng Shou
In this notebook paper, we describe our approach in the submission to the temporal action proposal (task 3) and temporal action localization (task 4) of ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action classification task is already very high (nearly 90% in ActivityNet dataset), we believe that the main bottleneck for temporal action localization is the quality of action proposals. Therefore, we mainly focus on the temporal action proposal task and propose a new proposal model based on temporal convolutional network. Our approach achieves the state-of-the-art performances on both temporal action proposal task and temporal action localization task.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temporal Action Proposal Generation | ActivityNet-1.3 | Lin et al. | AR@100 | 73.01 | #11 of 11 | Archive leaderboard | report |
| Temporal Action Proposal Generation | ActivityNet-1.3 | Lin et al. | AUC (test) | 64.80 | #11 of 11 | Archive leaderboard | report |
| Temporal Action Proposal Generation | ActivityNet-1.3 | Lin et al. | AUC (val) | 64.40 | #11 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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