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In an advancement for robotics and artificial intelligence, researchers at Chongqing University of Technology, along with their international collaborators, have developed a cutting-edge method for enhancing interaction recognition. ...
The current limitations of Graph Neural Networks. We continue our two part series on ML on Graphs, by asking: could graphs replace other domain specific formats and algorithms, such as Computer Vision (CV) or Natural Language Processing (NLP)?
This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate node information like a vanilla graph convolutional operation but also gradient information. Without introducing any additional parameters, CDGC can replace vanilla graph convolution in any existing Graph Convolutional Networks (GCNs). In addition, an accelerated version of the CDGC is developed which greatly improves the speed of training. Experiments on two popular large-scale datasets NTU RGB+D 60 & 120 have dem...