The Experts below are selected from a list of 69720 Experts worldwide ranked by ideXlab platform
Xinmei Tian - One of the best experts on this subject based on the ideXlab platform.
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Graph edge convolutional neural networks for skeleton based action recognition
2020Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:Body joints, directly obtained from a pose estimation model, have proven effective for action recognition. Existing works focus on analyzing the dynamics of human joints. However, except joints, humans also explore motions of limbs for understanding actions. Given this observation, we investigate the dynamics of human limbs for skeleton-based action recognition. Specifically, we represent an edge in a Graph of a human skeleton by integrating its spatial neighboring edges (for encoding the cooperation between different limbs) and its temporal neighboring edges (for achieving the consistency of movements in an action). Based on this new edge representation, we devise a Graph edge convolutional neural network (CNN). Considering the complementarity between Graph Node convolution and edge convolution, we further construct two hybrid networks by introducing different shared intermediate layers to integrate Graph Node and edge CNNs. Our contributions are twofold, Graph edge convolution and hybrid networks for integrating the proposed edge convolution and the conventional Node convolution. Experimental results on the Kinetics and NTU-RGB+D data sets demonstrate that our Graph edge convolution is effective at capturing the characteristics of actions and that our Graph edge CNN significantly outperforms the existing state-of-the-art skeleton-based action recognition methods.
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Graph edge convolutional neural networks for skeleton based action recognition
2018Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:This paper investigates body bones from skeleton data for skeleton based action recognition. Body joints, as the direct result of mature pose estimation technologies, are always the key concerns of traditional action recognition methods. However, instead of joints, we humans naturally identify how the human body moves according to shapes, lengths and places of bones, which are more obvious and stable for observation. Hence given Graphs generated from skeleton data, we propose to develop convolutions over Graph edges that correspond to bones in human skeleton. We describe an edge by integrating its spatial neighboring edges to explore the cooperation between different bones, as well as its temporal neighboring edges to address the consistency of movements in an action. A Graph edge convolutional neural network is then designed for skeleton based action recognition. Considering the complementarity between Graph Node convolution and Graph edge convolution, we additionally construct two hybrid neural networks to combine Graph Node convolutional neural network and Graph edge convolutional neural network using shared intermediate layers. Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our Graph edge convolution is effective to capture characteristic of actions and our Graph edge convolutional neural network significantly outperforms existing state-of-art skeleton based action recognition methods. Additionally, more performance improvements can be achieved by the hybrid networks.
Xikun Zhang - One of the best experts on this subject based on the ideXlab platform.
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Graph edge convolutional neural networks for skeleton based action recognition
2020Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:Body joints, directly obtained from a pose estimation model, have proven effective for action recognition. Existing works focus on analyzing the dynamics of human joints. However, except joints, humans also explore motions of limbs for understanding actions. Given this observation, we investigate the dynamics of human limbs for skeleton-based action recognition. Specifically, we represent an edge in a Graph of a human skeleton by integrating its spatial neighboring edges (for encoding the cooperation between different limbs) and its temporal neighboring edges (for achieving the consistency of movements in an action). Based on this new edge representation, we devise a Graph edge convolutional neural network (CNN). Considering the complementarity between Graph Node convolution and edge convolution, we further construct two hybrid networks by introducing different shared intermediate layers to integrate Graph Node and edge CNNs. Our contributions are twofold, Graph edge convolution and hybrid networks for integrating the proposed edge convolution and the conventional Node convolution. Experimental results on the Kinetics and NTU-RGB+D data sets demonstrate that our Graph edge convolution is effective at capturing the characteristics of actions and that our Graph edge CNN significantly outperforms the existing state-of-the-art skeleton-based action recognition methods.
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Graph edge convolutional neural networks for skeleton based action recognition
2018Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:This paper investigates body bones from skeleton data for skeleton based action recognition. Body joints, as the direct result of mature pose estimation technologies, are always the key concerns of traditional action recognition methods. However, instead of joints, we humans naturally identify how the human body moves according to shapes, lengths and places of bones, which are more obvious and stable for observation. Hence given Graphs generated from skeleton data, we propose to develop convolutions over Graph edges that correspond to bones in human skeleton. We describe an edge by integrating its spatial neighboring edges to explore the cooperation between different bones, as well as its temporal neighboring edges to address the consistency of movements in an action. A Graph edge convolutional neural network is then designed for skeleton based action recognition. Considering the complementarity between Graph Node convolution and Graph edge convolution, we additionally construct two hybrid neural networks to combine Graph Node convolutional neural network and Graph edge convolutional neural network using shared intermediate layers. Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our Graph edge convolution is effective to capture characteristic of actions and our Graph edge convolutional neural network significantly outperforms existing state-of-art skeleton based action recognition methods. Additionally, more performance improvements can be achieved by the hybrid networks.
Chang Xu - One of the best experts on this subject based on the ideXlab platform.
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Graph edge convolutional neural networks for skeleton based action recognition
2020Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:Body joints, directly obtained from a pose estimation model, have proven effective for action recognition. Existing works focus on analyzing the dynamics of human joints. However, except joints, humans also explore motions of limbs for understanding actions. Given this observation, we investigate the dynamics of human limbs for skeleton-based action recognition. Specifically, we represent an edge in a Graph of a human skeleton by integrating its spatial neighboring edges (for encoding the cooperation between different limbs) and its temporal neighboring edges (for achieving the consistency of movements in an action). Based on this new edge representation, we devise a Graph edge convolutional neural network (CNN). Considering the complementarity between Graph Node convolution and edge convolution, we further construct two hybrid networks by introducing different shared intermediate layers to integrate Graph Node and edge CNNs. Our contributions are twofold, Graph edge convolution and hybrid networks for integrating the proposed edge convolution and the conventional Node convolution. Experimental results on the Kinetics and NTU-RGB+D data sets demonstrate that our Graph edge convolution is effective at capturing the characteristics of actions and that our Graph edge CNN significantly outperforms the existing state-of-the-art skeleton-based action recognition methods.
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Graph edge convolutional neural networks for skeleton based action recognition
2018Co-Authors: Xikun Zhang, Chang Xu, Xinmei TianAbstract:This paper investigates body bones from skeleton data for skeleton based action recognition. Body joints, as the direct result of mature pose estimation technologies, are always the key concerns of traditional action recognition methods. However, instead of joints, we humans naturally identify how the human body moves according to shapes, lengths and places of bones, which are more obvious and stable for observation. Hence given Graphs generated from skeleton data, we propose to develop convolutions over Graph edges that correspond to bones in human skeleton. We describe an edge by integrating its spatial neighboring edges to explore the cooperation between different bones, as well as its temporal neighboring edges to address the consistency of movements in an action. A Graph edge convolutional neural network is then designed for skeleton based action recognition. Considering the complementarity between Graph Node convolution and Graph edge convolution, we additionally construct two hybrid neural networks to combine Graph Node convolutional neural network and Graph edge convolutional neural network using shared intermediate layers. Experimental results on Kinetics and NTU-RGB+D datasets demonstrate that our Graph edge convolution is effective to capture characteristic of actions and our Graph edge convolutional neural network significantly outperforms existing state-of-art skeleton based action recognition methods. Additionally, more performance improvements can be achieved by the hybrid networks.
Yongqing Zheng - One of the best experts on this subject based on the ideXlab platform.
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author name disambiguation using Graph Node embedding method
2019Co-Authors: Wenjing Zhang, Zhongmin Yan, Yongqing ZhengAbstract:In real-world, name ambiguity mainly arises when many people share the same name or express their names in the same way, which often causes erroneous aggregation of records of multiple persons with the same name. This name ambiguity problem deteriorates the performance of information retrieval in digital libraries, web search etc. It is nontrivial to distinguish those name references, especially when there is very limited information about them. Most existing studies uses features like email address, frequent words etc. However, the information is not always available because of privacy or too expensive to get. In this paper, we utilize a Graph Node embedding approach to solve author name disambiguation problem, where a Graph is constructed only using the collaborator relationships. In the methodological aspect, the proposed method uses random walk and a Graph Node representation learning method to embed each Node into a low dimensional vector space. Finally, we solve this problem by partitioning the records associated with a name reference such that each partition contains records pertaining to a unique real-world person. We evaluate our method on the real world CiteSeerX dataset, and the experimental results demonstrate that the proposed method is significantly better than most of the existing name disambiguation methods working in a similar setting.
Wenjing Zhang - One of the best experts on this subject based on the ideXlab platform.
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author name disambiguation using Graph Node embedding method
2019Co-Authors: Wenjing Zhang, Zhongmin Yan, Yongqing ZhengAbstract:In real-world, name ambiguity mainly arises when many people share the same name or express their names in the same way, which often causes erroneous aggregation of records of multiple persons with the same name. This name ambiguity problem deteriorates the performance of information retrieval in digital libraries, web search etc. It is nontrivial to distinguish those name references, especially when there is very limited information about them. Most existing studies uses features like email address, frequent words etc. However, the information is not always available because of privacy or too expensive to get. In this paper, we utilize a Graph Node embedding approach to solve author name disambiguation problem, where a Graph is constructed only using the collaborator relationships. In the methodological aspect, the proposed method uses random walk and a Graph Node representation learning method to embed each Node into a low dimensional vector space. Finally, we solve this problem by partitioning the records associated with a name reference such that each partition contains records pertaining to a unique real-world person. We evaluate our method on the real world CiteSeerX dataset, and the experimental results demonstrate that the proposed method is significantly better than most of the existing name disambiguation methods working in a similar setting.
