Yun Fu mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Feature extraction and Facial recognition system. His research investigates the link between Artificial intelligence and topics such as Computer vision that cross with problems in Classifier. His Pattern recognition research includes elements of Contextual image classification, Graph embedding and Residual.
His work deals with themes such as Representation and Natural language processing, which intersect with Machine learning. The concepts of his Feature extraction study are interwoven with issues in Image resolution, Principal component analysis and Hidden Markov model. His studies in Facial recognition system integrate themes in fields like Image processing, Regression analysis, Nonlinear dimensionality reduction and Pattern recognition.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Computer vision. His study in Feature extraction, Facial recognition system, Subspace topology, Cluster analysis and Contextual image classification is done as part of Artificial intelligence. His Pattern recognition study incorporates themes from Image, Feature and Robustness.
In most of his Machine learning studies, his work intersects topics such as Classifier. His research combines Deep learning and Discriminative model. Yun Fu is interested in Pose, which is a field of Computer vision.
Yun Fu spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Artificial neural network. All of his Artificial intelligence and Deep learning, Benchmark, Feature, Face and RGB color model investigations are sub-components of the entire Artificial intelligence study. His work carried out in the field of Pattern recognition brings together such families of science as Feature, Image compression and Image.
The Machine learning study combines topics in areas such as Contextual image classification, Matrix decomposition and Focus. When carried out as part of a general Computer vision research project, his work on Feature extraction and Pyramid is frequently linked to work in Diversity, therefore connecting diverse disciplines of study. Yun Fu studied Feature extraction and Imputation that intersect with Cluster analysis.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature extraction, Benchmark and Feature. His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His Machine learning research includes themes of Matrix decomposition and Modality.
His Classifier study in the realm of Pattern recognition connects with subjects such as Convexity. Yun Fu has included themes like Object detection and Minimum bounding box in his Feature extraction study. His Benchmark research is multidisciplinary, incorporating elements of State and Data science.
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Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Yulun Zhang;Kunpeng Li;Kai Li;Lichen Wang.
european conference on computer vision (2018)
Residual Dense Network for Image Super-Resolution
Yulun Zhang;Yapeng Tian;Yu Kong;Bineng Zhong.
computer vision and pattern recognition (2018)
Age Synthesis and Estimation via Faces: A Survey
Yun Fu;Guodong Guo;T S Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
Guodong Guo;Yun Fu;C.R. Dyer;T.S. Huang.
IEEE Transactions on Image Processing (2008)
Learning With $ll ^{1}$ -Graph for Image Analysis
Bin Cheng;Jianchao Yang;Shuicheng Yan;Yun Fu.
IEEE Transactions on Image Processing (2010)
Human age estimation using bio-inspired features
Guodong Guo;Guowang Mu;Yun Fu;Thomas S Huang.
computer vision and pattern recognition (2009)
Human Age Estimation With Regression on Discriminative Aging Manifold
Yun Fu;T.S. Huang.
IEEE Transactions on Multimedia (2008)
Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection
Mohammad Soleymani;Sadjad Asghari-Esfeden;Yun Fu;Maja Pantic.
IEEE Transactions on Affective Computing (2016)
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
Sheng Li;Jaya Kawale;Yun Fu.
conference on information and knowledge management (2015)
Large Scale Incremental Learning
Yue Wu;Yinpeng Chen;Lijuan Wang;Yuancheng Ye.
computer vision and pattern recognition (2019)
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