G-TAD: Sub-Graph Localization for Temporal Action Detection

Abstract

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 a 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 stateof-the-art performance on two detection benchmarks. On ActityNet-1.3, we obtain an average mAP of 34.09%; on THUMOS14, we obtain 40.16% in

Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

“…In recent years, graph convolutional networks (GNN) have drawn increasing attention due to their successful applications in various tasks [30]- [37], [40]- [42]. At the beginning, Scarselli et al [43] introduced the graph neural network for graph-focused and node-focused applications by extending recursive neural networks and random walk models.…”