Papers › Learning Latent Super-Events to Detect Multiple Activities in Videos
Learning Latent Super-Events to Detect Multiple Activities in Videos
AJ Piergiovanni, Michael S. Ryoo
In this paper, we introduce the concept of learning latent super-events from activity videos, and present how it benefits activity detection in continuous videos. We define a super-event as a set of multiple events occurring together in videos with a particular temporal organization; it is the opposite concept of sub-events. Real-world videos contain multiple activities and are rarely segmented (e.g., surveillance videos), and learning latent super-events allows the model to capture how the events are temporally related in videos. We design temporal structure filters that enable the model to focus on particular sub-intervals of the videos, and use them together with a soft attention mechanism to learn representations of latent super-events. Super-event representations are combined with per-frame or per-segment CNNs to provide frame-level annotations. Our approach is designed to be fully differentiable, enabling end-to-end learning of latent super-event representations jointly with the activity detector using them. Our experiments with multiple public video datasets confirm that the proposed concept of latent super-event learning significantly benefits activity detection, advancing the state-of-the-arts.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Detection | Charades | Super-events (RGB+Flow) | mAP | 19.41 | #14 of 16 | Archive leaderboard | report |
| Action Detection | Multi-THUMOS | I3D + our super-event | mAP | 36.4 | #6 of 8 | Archive leaderboard | report |
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