2019 - IEEE Fellow For contributions to multimedia analysis and applications
2016 - ACM Distinguished Member
2012 - ACM Senior Member
His primary areas of study are Artificial intelligence, Machine learning, Convolutional neural network, Pattern recognition and Information retrieval. His work in Artificial intelligence addresses issues such as Natural language processing, which are connected to fields such as Semantics. His Machine learning research incorporates themes from Domain and Context.
The concepts of his Convolutional neural network study are interwoven with issues in Recurrent neural network, Feature learning and Inference. The various areas that Tao Mei examines in his Pattern recognition study include Image, Representation and Categorization. His research investigates the connection between Information retrieval and topics such as Ranking that intersect with issues in Visual search, Search engine, Beam search, Incremental heuristic search and Phrase search.
Tao Mei spends much of his time researching Artificial intelligence, Machine learning, Pattern recognition, Information retrieval and Computer vision. His Artificial intelligence research focuses on Natural language processing and how it connects with Semantics. The Machine learning study combines topics in areas such as Domain, Training set and TRECVID.
His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification and Parsing. Tao Mei has researched Feature extraction in several fields, including Visualization and Feature. Tao Mei has included themes like Encoder, Recurrent neural network, Natural language and Speech recognition in his Closed captioning study.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Facial recognition system. Artificial intelligence is closely attributed to Code in his research. As part of one scientific family, Tao Mei deals mainly with the area of Machine learning, narrowing it down to issues related to the Domain, and often Feature.
His Pattern recognition research is multidisciplinary, incorporating elements of Artificial neural network, Similarity, Parsing and Generative grammar. The study incorporates disciplines such as Representation and Polygon mesh in addition to Computer vision. His biological study spans a wide range of topics, including Margin, Speech recognition and Softmax function.
Tao Mei focuses on Artificial intelligence, Discriminative model, Machine learning, Pattern recognition and Feature extraction. His work on Computer vision expands to the thematically related Artificial intelligence. His Discriminative model research integrates issues from Minimum bounding box and Code.
Tao Mei interconnects Domain, Facial recognition system and Closed captioning in the investigation of issues within Machine learning. His work on Training set as part of general Pattern recognition study is frequently linked to Dimension, therefore connecting diverse disciplines of science. Tao Mei works mostly in the field of Feature extraction, limiting it down to concerns involving Task analysis and, occasionally, Contrast and Matching.
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.
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition
Jianlong Fu;Heliang Zheng;Tao Mei.
computer vision and pattern recognition (2017)
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Zhaofan Qiu;Ting Yao;Tao Mei.
international conference on computer vision (2017)
Correlative multi-label video annotation
Guo-Jun Qi;Xian-Sheng Hua;Yong Rui;Jinhui Tang.
acm multimedia (2007)
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Jun Xu;Tao Mei;Ting Yao;Yong Rui.
computer vision and pattern recognition (2016)
Boosting Image Captioning with Attributes
Ting Yao;Yingwei Pan;Yehao Li;Zhaofan Qiu.
international conference on computer vision (2017)
Multiview Spectral Embedding
Tian Xia;Dacheng Tao;Tao Mei;Yongdong Zhang.
systems man and cybernetics (2010)
Jointly Modeling Embedding and Translation to Bridge Video and Language
Yingwei Pan;Tao Mei;Ting Yao;Houqiang Li.
computer vision and pattern recognition (2016)
Personalized Recommendation Combining User Interest and Social Circle
Xueming Qian;He Feng;Guoshuai Zhao;Tao Mei.
IEEE Transactions on Knowledge and Data Engineering (2014)
Exploring Visual Relationship for Image Captioning
Ting Yao;Yingwei Pan;Yehao Li;Tao Mei.
european conference on computer vision (2018)
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
Heliang Zheng;Jianlong Fu;Tao Mei;Jiebo Luo.
international conference on computer vision (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
University of Science and Technology of China
Microsoft (United States)
Chinese University of Hong Kong, Shenzhen
Lenovo (China)
University of Science and Technology of China
University of Rochester
Nanjing University of Science and Technology
Microsoft (United States)
Singapore Management University
University of Science and Technology of China
George Mason University
ICON Health & Fitness, Inc.
Stanford University
Harbin Engineering University
University of Parma
Korea Advanced Institute of Science and Technology
Temple University
California State University, Fullerton
National Institutes of Health
University of Montpellier
University of Verona
Utrecht University
Instituto de Salud Carlos III
Glasgow Royal Infirmary
Johns Hopkins University
University of California, Los Angeles