Papers › VideoGraph: Recognizing Minutes-Long Human Activities in Videos
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Many human activities take minutes to unfold. To represent them, related works opt for statistical pooling, which neglects the temporal structure. Others opt for convolutional methods, as CNN and Non-Local. While successful in learning temporal concepts, they are short of modeling minutes-long temporal dependencies. We propose VideoGraph, a method to achieve the best of two worlds: represent minutes-long human activities and learn their underlying temporal structure. VideoGraph learns a graph-based representation for human activities. The graph, its nodes and edges are learned entirely from video datasets, making VideoGraph applicable to problems without node-level annotation. The result is improvements over related works on benchmarks: Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to learn the temporal structure of human activities in minutes-long videos.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-video Activity Recognition | Breakfast | VideoGraph (I3D-K400-Pretrain-feature) | mAP | 63.14 | #6 of 8 | Archive leaderboard | report |
| Video Classification | Breakfast | VideoGraph | Accuracy (%) | 69.5 | #9 of 9 | 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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