The scientist’s investigation covers issues in Artificial intelligence, Recurrent neural network, Pattern recognition, Computer vision and Machine learning. His Artificial intelligence study frequently links to adjacent areas such as Construct. His work carried out in the field of Recurrent neural network brings together such families of science as End-to-end principle, Frame and Joint.
His work in the fields of Feature learning and Feature extraction overlaps with other areas such as Set. His work on JPEG, Light-field camera and Image quality as part of general Computer vision study is frequently linked to Multivariate interpolation, therefore connecting diverse disciplines of science. His research integrates issues of Ranging, Single image and Data mining in his study of Machine learning.
Wenjun Zeng focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Frame. His work in Recurrent neural network, Feature, Benchmark, Feature learning and Discriminative model are all subfields of Artificial intelligence research. The Pattern recognition study combines topics in areas such as Contextual image classification, Pyramid and Feature.
His Computer vision study incorporates themes from Detector and Code. His Machine learning study typically links adjacent topics like Key. Wenjun Zeng interconnects Margin and Robustness in the investigation of issues within Video tracking.
Artificial intelligence, Pattern recognition, Machine learning, Feature and Computer vision are his primary areas of study. His Feature learning, Discriminative model, Pose, Object detection and Re identification study are his primary interests in Artificial intelligence. Many of his research projects under Pattern recognition are closely connected to Sample with Sample, tying the diverse disciplines of science together.
The concepts of his Machine learning study are interwoven with issues in Collapse and Inference. His research on Feature also deals with topics like
His primary areas of investigation include Artificial intelligence, Object detection, Machine learning, Pattern recognition and Computer vision. His Artificial intelligence study combines topics in areas such as Frame and Identity. His work deals with themes such as Artificial neural network, Detector and Thesaurus, which intersect with Frame.
His Pattern recognition research includes themes of Recurrent neural nets, Entropy, Entropy, Residual and Ranking. His work focuses on many connections between Computer vision and other disciplines, such as Code, that overlap with his field of interest in Contrast, Multi camera and Pose. His research investigates the connection with Semantics and areas like Feature which intersect with concerns in Benchmark, Discriminative model and Feature extraction.
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An end-to-end spatio-temporal attention model for human action recognition from skeleton data
Sijie Song;Cuiling Lan;Junliang Xing;Wenjun Zeng.
national conference on artificial intelligence (2017)
Benchmarking Single-Image Dehazing and Beyond
Boyi Li;Wenqi Ren;Dengpan Fu;Dacheng Tao.
IEEE Transactions on Image Processing (2019)
The sixth visual object tracking VOT2018 challenge results
Matej Kristan;Aleš Leonardis;Jiří Matas;Michael Felsberg.
european conference on computer vision (2019)
A Twofold Siamese Network for Real-Time Object Tracking
Anfeng He;Chong Luo;Xinmei Tian;Wenjun Zeng.
computer vision and pattern recognition (2018)
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Wentao Zhu;Cuiling Lan;Junliang Xing;Wenjun Zeng.
arXiv: Computer Vision and Pattern Recognition (2016)
A format-compliant configurable encryption framework for access control of video
Jiangtao Wen;M. Severa;Wenjun Zeng;M.H. Luttrell.
IEEE Transactions on Circuits and Systems for Video Technology (2002)
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Pengfei Zhang;Cuiling Lan;Junliang Xing;Wenjun Zeng.
international conference on computer vision (2017)
Digital image scrambling for image coding systems
Shaw-Min Lei;Wenjun Zeng.
Multimedia Security Technologies for Digital Rights Management
Wenjun Zeng;Heather Yu;Ching-Yung Lin.
Geometric-structure-based error concealment with novel applications in block-based low-bit-rate coding
Wenjun Zeng;Bede Liu.
IEEE Transactions on Circuits and Systems for Video Technology (1999)
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