J
Jianru Xue
Researcher at Xi'an Jiaotong University
Publications - 212
Citations - 4885
Jianru Xue is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Image segmentation & Mobile robot. The author has an hindex of 27, co-authored 203 publications receiving 3314 citations. Previous affiliations of Jianru Xue include Microsoft & Chang'an University.
Papers
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Proceedings ArticleDOI
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
TL;DR: Zhang et al. as discussed by the authors proposed a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Proceedings ArticleDOI
SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction
TL;DR: Zhang et al. as mentioned in this paper propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.
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View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
TL;DR: A novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action by design a view adaptive recurrent neural network with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Journal ArticleDOI
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition
TL;DR: Zhang et al. as discussed by the authors proposed two view adaptive neural networks, i.e., VA-RNN and VA-CNN, which are respectively built based on the recurrent neural network (RNN) with the Long Short-term Memory (LSTM) and the CNN.
Proceedings ArticleDOI
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
TL;DR: In this paper, a semantics-guided neural network (SGN) is proposed for skeleton-based action recognition, which explicitly introduces the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability.