  Temporal action detection is a fundamental yet challenging task in video
understanding. Video context is a critical cue to effectively detect actions,
but current works mainly focus on temporal context, while neglecting semantic
context as well as other important context properties. In this work, we propose
a graph convolutional network (GCN) model to adaptively incorporate multi-level
semantic context into video features and cast temporal action detection as a
sub-graph localization problem. Specifically, we formulate video snippets as
graph nodes, snippet-snippet correlations as edges, and actions associated with
context as target sub-graphs. With graph convolution as the basic operation, we
design a GCN block called GCNeXt, which learns the features of each node by
aggregating its context and dynamically updates the edges in the graph. To
localize each sub-graph, we also design an SGAlign layer to embed each
sub-graph into the Euclidean space. Extensive experiments show that G-TAD is
capable of finding effective video context without extra supervision and
achieves state-of-the-art performance on two detection benchmarks. On
ActivityNet-1.3, it obtains an average mAP of 34.09%; on THUMOS14, it reaches
51.6% at<EMAIL_ADDRESS>when combined with a proposal processing method. G-TAD code is
publicly available at https://github.com/frostinassiky/gtad.
