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Enhancing interaction recognition: The power of merge-and-split graph convolutional networks

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. . ....

Shaoqiang Wang , Graph Convolutional Network For Skeleton , School Of Artificial Intelligence , University Of Technology , Graph Convolutional Network , Chongqing University , Artificial Intelligence , Bionic Systems , Merge And Split Graph Convolutional Network , Merge And Split Graph Convolution , Hierarchical Guided Attention , Short Term Dependence , Skeleton Based Interaction Recognition ,

Revamping Interaction Recognition via Merge-Split Graph Networks

Revamping Interaction Recognition via Merge-Split Graph Networks
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University Of Technology , Graph Convolutional Network For Skeleton , Graph Convolutional Network , Chongqing University , Bionic Systems , Merge And Split Graph Convolutional Network , Merge And Split Graph Convolution , Hierarchical Guided Attention , Short Term Dependence , Split Graph Structure , Guided Attention , Dependence Module , Split Graph Convolutional Network , Skeleton Based Interaction Recognition ,

The dark side of Graph Neural Networks

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)? ....

Graph Neural Networks , Message Passing Neural Networks , Graph Neural Network , Convolutional Neural Networks Or Transformers , Graph Convolutional Network , Why Machine Learning , Computer Vision , Natural Language Processing , Convolutional Neural Networks , Hardware Lottery , Machine Learning , Graph Neural , Quantum Deep , Jure Leskovec ,

"A Central Difference Graph Convolutional Operator for Skeleton-Based A" by Shuangyan Miao, Yonghong Hou et al.

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 demonstrated the efficacy of the proposed CDGC. Code is available at https://github.com/iesymiao/CD-GCN. ....

Graph Convolutional Networks Gcns , Graph Convolutional Networks , Action Recognition , Onvolutional Codes , Graph Convolutional Network ,