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Many human activities take minutes to unfold. To represent them, relatedworks opt for statistical pooling, which neglects the temporal structure.Others opt for convolutional methods, as CNN and Non-Local. While successful inlearning temporal concepts, they are short of modeling minutes-long temporaldependencies. We propose VideoGraph, a method to achieve the best of twoworlds: represent minutes-long human activities and learn their underlyingtemporal structure. VideoGraph learns a graph-based representation for humanactivities. The graph, its nodes and edges are learned entirely from videodatasets, making VideoGraph applicable to problems without node-levelannotation. The result is improvements over related works on benchmarks:Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able tolearn the temporal structure of human activities in minutes-long videos.

VideoGraph: Recognizing Minutes-Long Human Activities in Videos