Papers › Cascaded Boundary Regression for Temporal Action Detection
Cascaded Boundary Regression for Temporal Action Detection
Jiyang Gao, Zhenheng Yang, Ram Nevatia
Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. We adapt a two-stage temporal action detection pipeline with Cascaded Boundary Regression (CBR) model. Class-agnostic proposals and specific actions are detected respectively in the first and the second stage. CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows. The salient aspect of the refinement process is that, inside each stage, the temporal boundaries are adjusted in a cascaded way by feeding the refined windows back to the system for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and achieve state-of-the-art performance on both datasets. The performance gain is especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14 is improved from 19.0% to 31.0%.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.1 | 60.1 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.2 | 56.7 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.3 | 50.1 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.4 | 41.3 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.5 | 31 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.6 | 19.1 | #34 of 42 | Archive leaderboard | report |
| Temporal Action Localization | THUMOS’14 | CBR-TS | mAP IOU@0.7 | 9.9 | #34 of 42 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections