What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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 most cited work include:
- Residual Dense Network for Image Super-Resolution (925 citations)
- Image Super-Resolution Using Very Deep Residual Channel Attention Networks (902 citations)
- Age Synthesis and Estimation via Faces: A Survey (565 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (79.44%)
- Pattern recognition (36.73%)
- Machine learning (32.14%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.44%)
- Pattern recognition (36.73%)
- Machine learning (32.14%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Residual Dense Network for Image Restoration. (109 citations)
- TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution (72 citations)
- Rethinking Classification and Localization for Object Detection (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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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