Latest Breaking News On - Graph convolutional network - Page 1 : comparemela.com

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 wangGraph convolutional network for skeletonSchool of artificial intelligenceUniversity of technologyGraph convolutional networkChongqing universityArtificial intelligenceBionic systemsMerge and split graph convolutional networkMerge and split graph convolutionHierarchical guided attentionShort term dependenceSkeleton based interaction recognition

Revamping Interaction Recognition via Merge-Split Graph Networks

Revamping Interaction Recognition via Merge-Split Graph Networks
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

University of technologyGraph convolutional network for skeletonGraph convolutional networkChongqing universityBionic systemsMerge and split graph convolutional networkMerge and split graph convolutionHierarchical guided attentionShort term dependenceSplit graph structureGuided attentionDependence moduleSplit graph convolutional networkSkeleton 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 networksMessage passing neural networksGraph neural networkConvolutional neural networks or transformersGraph convolutional networkWhy machine learningComputer visionNatural language processingConvolutional neural networksHardware lotteryMachine learningGraph neuralQuantum deepJure 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 gcnsGraph convolutional networksAction recognitionOnvolutional codesGraph convolutional network