Papers › Videos as Space-Time Region Graphs

Videos as Space-Time Region Graphs

5 Jun 2018ECCV 2018 9arXiv:1806.01810archive 2025-07-28

Xiaolong Wang, Abhinav Gupta

How do humans recognize the action "opening a book" ? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph nodes are defined by the object region proposals from different frames in a long range video. These nodes are connected by two types of relations: (i) similarity relations capturing the long range dependencies between correlated objects and (ii) spatial-temporal relations capturing the interactions between nearby objects. We perform reasoning on this graph representation via Graph Convolutional Networks. We achieve state-of-the-art results on both Charades and Something-Something datasets. Especially for Charades, we obtain a huge 4.4% gain when our model is applied in complex environments.

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Tasks

Action ClassificationAction Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Classification Charades STRG MAP 39.7 #34 of 49 Archive leaderboard report
Action Recognition Something-Something V1 NL I3D + GCN Top 1 Accuracy 46.1 #68 of 74 Archive leaderboard report

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Methods

Graph Convolutional Networks

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