His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Motion capture. The study of Artificial intelligence is intertwined with the study of Algorithm in a number of ways. His Pattern recognition research incorporates elements of Facial recognition system, Speech recognition and Categorization.
His study looks at the relationship between Convolutional neural network and topics such as Object, which overlap with Benchmark. His Motion capture research integrates issues from Dynamic time warping, Image segmentation, Kernel, Cluster analysis and Computational model. The concepts of his Feature study are interwoven with issues in Discriminative model and Constraint.
Feng Zhou spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model. His is doing research in Feature, Benchmark, Object, Convolutional neural network and Feature extraction, both of which are found in Artificial intelligence. Feng Zhou has researched Pattern recognition in several fields, including Facial recognition system, Categorization and Cluster analysis.
His study in Machine learning is interdisciplinary in nature, drawing from both Mean squared error and Similarity. His work on Image and Motion capture as part of general Computer vision research is often related to Process and Position, thus linking different fields of science. His Image research is multidisciplinary, relying on both Algorithm and Theoretical computer science.
Feng Zhou mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Facial recognition system and Feature. His biological study spans a wide range of topics, including Task and Computer vision. His study in the fields of Discriminative model and Classifier under the domain of Pattern recognition overlaps with other disciplines such as Color doppler, Maternal health and Grading.
His Machine learning study which covers Mean squared error that intersects with Eye tracking, Gradient boosting and Tree. His Facial recognition system research includes themes of Boosting, Spoofing attack and Feature extraction. His studies deal with areas such as Matching, Computation and Theoretical computer science as well as Feature.
Feng Zhou mostly deals with Artificial intelligence, Machine learning, Facial recognition system, Pattern recognition and Neuroimaging. Feature is the focus of his Artificial intelligence research. The various areas that Feng Zhou examines in his Machine learning study include Multi-task learning and Task analysis.
His research integrates issues of Feature extraction and Spoofing attack in his study of Facial recognition system. Feng Zhou interconnects Boosting, Robustness and Benchmark in the investigation of issues within Feature extraction. Feng Zhou is studying Discriminative model, which is a component of 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.
Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR
Feng Zhou;Henry Been-Lirn Duh;Mark Billinghurst.
international symposium on mixed and augmented reality (2008)
Detecting depression from facial actions and vocal prosody
Jeffrey F. Cohn;Tomas Simon Kruez;Iain Matthews;Ying Yang.
affective computing and intelligent interaction (2009)
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion
Feng Zhou;F. De la Torre;J. K. Hodgins.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Deep Metric Learning with Angular Loss
Jian Wang;Feng Zhou;Shilei Wen;Xiao Liu.
international conference on computer vision (2017)
Canonical Time Warping for Alignment of Human Behavior
Feng Zhou;Fernando Torre.
neural information processing systems (2009)
Kernel Pooling for Convolutional Neural Networks
Yin Cui;Feng Zhou;Jiang Wang;Xiao Liu.
computer vision and pattern recognition (2017)
Aligned Cluster Analysis for temporal segmentation of human motion
Feng Zhou;F. Torre;J.K. Hodgins.
ieee international conference on automatic face & gesture recognition (2008)
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Ming Sun;Yuchen Yuan;Feng Zhou;Errui Ding.
european conference on computer vision (2018)
Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop
Yin Cui;Feng Zhou;Yuanqing Lin;Serge Belongie.
computer vision and pattern recognition (2016)
Generalized time warping for multi-modal alignment of human motion
Feng Zhou;Fernando De la Torre.
computer vision and pattern recognition (2012)
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: