Jun Liu mainly investigates Chemical engineering, Artificial intelligence, Photochemistry, Nanotechnology and Polymer. His biological study spans a wide range of topics, including Inorganic chemistry, Organic chemistry, Anode and Doping. His Artificial intelligence research includes themes of Machine learning and Pattern recognition.
His Photochemistry research incorporates themes from Organic semiconductor and Electroluminescence, Polyfluorene. Jun Liu interconnects Electrode and Lithium in the investigation of issues within Nanotechnology. Jun Liu combines subjects such as HOMO/LUMO and Electron acceptor with his study of Polymer.
Jun Liu mostly deals with Chemical engineering, Artificial intelligence, Polymer, Optoelectronics and Internal medicine. Chemical engineering is frequently linked to Nanotechnology in his study. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Computer vision and Pattern recognition.
His work deals with themes such as Photochemistry, Acceptor and HOMO/LUMO, which intersect with Polymer. Much of his study explores Internal medicine relationship to Endocrinology. His Conjugated system research includes elements of Side chain and Polymer chemistry.
His main research concerns Polymer, Artificial intelligence, Chemical engineering, Optoelectronics and Conjugated system. His Polymer research incorporates elements of Acceptor, HOMO/LUMO and Energy conversion efficiency. Jun Liu has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His work carried out in the field of Pattern recognition brings together such families of science as Noise and Feature. Many of his studies on Chemical engineering apply to Work as well. His Conjugated system research is multidisciplinary, relying on both Thermoelectric effect and Doping.
Jun Liu mostly deals with Chemical engineering, Artificial intelligence, Polymer, Radiology and Pattern recognition. His research integrates issues of Photocatalysis, Catalysis, Doping and Surface engineering in his study of Chemical engineering. His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision.
As a member of one scientific family, Jun Liu mostly works in the field of Machine learning, focusing on RGB color model and, on occasion, Feature extraction. The study incorporates disciplines such as Optoelectronics and Acceptor in addition to Polymer. His studies deal with areas such as Noise and Feature as well as Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing.
Xingzhi Xie;Zheng Zhong;Wei Zhao;Chao Zheng.
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Jun Liu;Amir Shahroudy;Dong Xu;Gang Wang.
european conference on computer vision (2016)
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Amir Shahroudy;Jun Liu;Tian-Tsong Ng;Gang Wang.
computer vision and pattern recognition (2016)
Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study.
Wei Zhao;Zheng Zhong;Xingzhi Xie;Qizhi Yu.
American Journal of Roentgenology (2020)
Tumor development is associated with decrease of TET gene expression and 5-methylcytosine hydroxylation
Yang H;Liu Y;Bai F;Zhang Jy.
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Jun Liu;Gang Wang;Ping Hu;Ling-Yu Duan.
computer vision and pattern recognition (2017)
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Jun Liu;Amir Shahroudy;Mauricio Perez;Gang Wang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
Jun Liu;Gang Wang;Ling-Yu Duan;Kamila Abdiyeva.
IEEE Transactions on Image Processing (2018)
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Jun Liu;Amir Shahroudy;Dong Xu;Alex C. Kot.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
Synthesis of Mo2N nanolayer coated MoO2 hollow nanostructures as high-performance anode materials for lithium-ion batteries
Jun Liu;Shasha Tang;Yakun Lu;Gemei Cai.
Energy and Environmental Science (2013)
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