Papers › UntrimmedNets for Weakly Supervised Action Recognition and Detection
UntrimmedNets for Weakly Supervised Action Recognition and Detection
Limin Wang, Yuanjun Xiong, Dahua Lin, Luc van Gool
Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. These two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
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 |
|---|---|---|---|---|---|---|---|
| Action Classification | ActivityNet-1.2 | UntrimmedNets | mAP | 87.7 | #3 of 3 | Archive leaderboard | report |
| Action Classification | THUMOS’14 | UntrimmedNets | mAP | 82.2 | #3 of 3 | Archive leaderboard | report |
| Weakly Supervised Action Localization | THUMOS 2014 | UntrimmedNets | mAP@0.1:0.7 | - | #29 of 30 | Archive leaderboard | report |
| Weakly Supervised Action Localization | THUMOS 2014 | UntrimmedNets | mAP@0.5 | 13.7 | #29 of 30 | 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