His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning. Yihong Gong combines subjects such as Matrix decomposition and Data mining with his study of Artificial intelligence. His work focuses on many connections between Pattern recognition and other disciplines, such as Object detection, that overlap with his field of interest in Pruning, Object Class and Simulated annealing.
He usually deals with Computer vision and limits it to topics linked to Metric and Edge detection and Image resolution. His studies in Feature extraction integrate themes in fields like Object, Motion, Convolutional neural network and Robustness. His Contextual image classification research integrates issues from Histogram and Neural coding.
Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Convolutional neural network are his primary areas of study. His work in Discriminative model, Feature extraction, Image, Contextual image classification and Artificial neural network are all subfields of Artificial intelligence research. His work on Video tracking, Superresolution, Tracking and Image resolution as part of his general Computer vision study is frequently connected to Trajectory, thereby bridging the divide between different branches of science.
His studies deal with areas such as Object detection and Feature as well as Pattern recognition. His work in Machine learning addresses issues such as Benchmark, which are connected to fields such as MNIST database. Yihong Gong interconnects Cognitive neuroscience of visual object recognition, Similarity, Margin, Face and Deep learning in the investigation of issues within Convolutional neural network.
Artificial intelligence, Pattern recognition, Machine learning, Convolutional neural network and Computer vision are his primary areas of study. His research in the fields of Feature extraction and Anomaly detection overlaps with other disciplines such as Masking. His work investigates the relationship between Feature extraction and topics such as Contextual image classification that intersect with problems in Softmax function.
His work on Ranking as part of general Machine learning study is frequently connected to Code, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Convolutional neural network study incorporates themes from Similarity, Feature vector, Probabilistic logic, Statistical model and Range. His study in the field of Tracking, Matching and RGB color model is also linked to topics like Trajectory.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature vector, Margin and Feature extraction. The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways. Yihong Gong is involved in the study of Pattern recognition that focuses on Discriminative model in particular.
The concepts of his Feature vector study are interwoven with issues in Similarity, Convolutional neural network, Hebbian theory and Topology. His Convolutional neural network research integrates issues from Contextual image classification and Training set. His Margin research focuses on Ground truth and how it relates to Function, Algorithm, Kernel and Kernel.
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Locality-constrained Linear Coding for image classification
Jinjun Wang;Jianchao Yang;Kai Yu;Fengjun Lv.
computer vision and pattern recognition (2010)
Linear spatial pyramid matching using sparse coding for image classification
Jianchao Yang;Kai Yu;Yihong Gong;Thomas Huang.
computer vision and pattern recognition (2009)
Document clustering based on non-negative matrix factorization
Wei Xu;Xin Liu;Yihong Gong.
international acm sigir conference on research and development in information retrieval (2003)
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
De Cheng;Yihong Gong;Sanping Zhou;Jinjun Wang.
computer vision and pattern recognition (2016)
Generic text summarization using relevance measure and latent semantic analysis
Yihong Gong;Xin Liu.
international acm sigir conference on research and development in information retrieval (2001)
Nonlinear Learning using Local Coordinate Coding
Kai Yu;Tong Zhang;Yihong Gong.
neural information processing systems (2009)
Automatic parsing of TV soccer programs
Yihong Gong;Lim Teck Sin;Chua Hock Chuan;Hongjiang Zhang.
international conference on multimedia computing and systems (1995)
Human Tracking Using Convolutional Neural Networks
Jialue Fan;Wei Xu;Ying Wu;Yihong Gong.
IEEE Transactions on Neural Networks (2010)
SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution
Shengyang Dai;Mei Han;Wei Xu;Ying Wu.
IEEE Transactions on Image Processing (2009)
Detecting communities and their evolutions in dynamic social networks--a Bayesian approach
Tianbao Yang;Yun Chi;Shenghuo Zhu;Yihong Gong.
Machine Learning (2011)
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