Papers › Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Zhekun Luo, Devin Guillory, Baifeng Shi, Wei Ke, Fang Wan, Trevor Darrell, Huijuan Xu
Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known, the main challenge is assigning which key instances within the bag to trigger the bag's label. Most previous models use attention-based approaches applying attentions to generate the bag's representation from instances, and then train it via the bag's classification. These models, however, implicitly violate the MIL assumption that instances in negative bags should be uniformly negative. In this work, we explicitly model the key instances assignment as a hidden variable and adopt an Expectation-Maximization (EM) framework. We derive two pseudo-label generation schemes to model the E and M process and iteratively optimize the likelihood lower bound. We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions. It achieves state-of-the-art performance on two standard benchmarks, THUMOS14 and ActivityNet1.2.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weakly Supervised Action Localization | THUMOS14 | EM-ML | avg-mAP (0.1-0.5) | 44.9 | #11 of 12 | Archive leaderboard | report |
| Weakly Supervised Action Localization | THUMOS14 | EM-ML | avg-mAP (0.1:0.7) | 37.7 | #11 of 12 | Archive leaderboard | report |
| Weakly Supervised Action Localization | THUMOS14 | EM-ML | avg-mAP (0.3-0.7) | 30.4 | #11 of 12 | Archive leaderboard | report |
| Weakly Supervised Action Localization | THUMOS’14 | EM-MIL | mAP@0.5 | 30.5 | #10 of 13 | 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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