Papers › Boundary-sensitive Pre-training for Temporal Localization in Videos
Boundary-sensitive Pre-training for Temporal Localization in Videos
Mengmeng Xu, Juan-Manuel Perez-Rua, Victor Escorcia, Brais Martinez, Xiatian Zhu, Li Zhang, Bernard Ghanem, Tao Xiang
Many video analysis tasks require temporal localization thus detection of content changes. However, most existing models developed for these tasks are pre-trained on general video action classification tasks. This is because large scale annotation of temporal boundaries in untrimmed videos is expensive. Therefore no suitable datasets exist for temporal boundary-sensitive pre-training. In this paper for the first time, we investigate model pre-training for temporal localization by introducing a novel boundary-sensitive pretext (BSP) task. Instead of relying on costly manual annotations of temporal boundaries, we propose to synthesize temporal boundaries in existing video action classification datasets. With the synthesized boundaries, BSP can be simply conducted via classifying the boundary types. This enables the learning of video representations that are much more transferable to downstream temporal localization tasks. Extensive experiments show that the proposed BSP is superior and complementary to the existing action classification based pre-training counterpart, and achieves new state-of-the-art performance on several temporal localization tasks.
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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 Localization | ActivityNet-1.3 | G-TAD+BSP | mAP | 34.75 | #24 of 33 | Archive leaderboard | report |
| Temporal Action Localization | ActivityNet-1.3 | G-TAD+BSP | mAP IOU@0.5 | 50.94 | #24 of 33 | Archive leaderboard | report |
| Temporal Action Localization | ActivityNet-1.3 | G-TAD+BSP | mAP IOU@0.75 | 35.61 | #24 of 33 | Archive leaderboard | report |
| Temporal Action Localization | ActivityNet-1.3 | G-TAD+BSP | mAP IOU@0.95 | 7.98 | #24 of 33 | 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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