Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition

By Ke Cheng, Yifan Zhang, Congqi Cao, Lei Shi, Jian Cheng, Hanqing Lu. European Conference on Computer Vision, 2020

338 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition (2021, methodology): “Furthermore, the study in [32] provides a decoupling GCN to boost the graph”
Human Action Recognition From Various Data Modalities: A Review (2020, background): “More recently, GCN-basedHAR has become a hot research direction [188], [189], [190], [191], [192].”
InfoGCN: Representation Learning for Human Skeleton-based Action Recognition (2022, neutral): “Lie Group [52] 74.2 Actionlet ensemble [54] 76.0 HBRNN-L [11] 78.5 Ensemble TS-LSTM [24] 89.2 AGC-LSTM [45] 93.3 Shift-GCN [9] 94.6 DC-GCN+ADG [8] 95.3 CTR-GCN [6] 96.5 Ours 97.0 Table 1.”
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition (2021, background): “[3] proposed a DCGCN which sets individual parameterized topologies for different channel groups.”