Titlebook: Image and Graphics; 10th International C Yao Zhao,Nick Barnes,Chunyu Lin Conference proceedings 2019 Springer Nature Switzerland AG 2019 ar

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Hierarchical Graph Convolutional Network for Skeleton-Based Action Recognitiony ignore the topological structure of the skeleton which is very important for action recognition. Recently, Graph Convolutional Networks (GCNs) achieve remarkable performance in modeling non-Euclidean structures. However, current graph convolutional networks lack the capacity of modeling hierarchic

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Semantic Segmentation of Street Scenes Using Disparity Informationlent results on several semantic segmentation benchmarks. Most of them, however, only exploit RGB information. Due to the development of stereo matching algorithms, disparity maps can be more easily acquired. Structural information encoded in disparity can be treated as supplementary information of

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Residual Joint Attention Network with Graph Structure Inference for Object Detectionng on the improvement of the feature extraction, we propose Residual Joint Attention Network, a convolutional neural network using a residual joint attention module which is composed of a spatial attention branch, a channel attention branch, and a residual learning branch within an advanced object d