What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Shuicheng Yan mostly deals with Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Machine learning.
His study in Object detection, Convolutional neural network, Facial recognition system, Discriminative model and Image segmentation falls within the category of Artificial intelligence.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Contextual image classification and Subspace topology.
His research in Feature extraction intersects with topics in Image processing, Histogram, Visualization, Iterative reconstruction and Robustness.
His Computer vision study integrates concerns from other disciplines, such as Representation and Benchmark.
His work on Feature as part of general Machine learning study is frequently linked to Context model, therefore connecting diverse disciplines of science.
His most cited work include:
- Face recognition using Laplacianfaces (2852 citations)
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction (2213 citations)
- Robust Recovery of Subspace Structures by Low-Rank Representation (2079 citations)
What are the main themes of his work throughout his whole career to date?
Shuicheng Yan focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction.
All of his Artificial intelligence and Discriminative model, Facial recognition system, Contextual image classification, Image and Face investigations are sub-components of the entire Artificial intelligence study.
His studies in Pattern recognition integrate themes in fields like Subspace topology, Feature and Object detection.
His Subspace topology research is multidisciplinary, incorporating perspectives in Linear subspace and Robustness.
His Computer vision study frequently links to adjacent areas such as Representation.
His work carried out in the field of Machine learning brings together such families of science as Parsing and Benchmark.
He most often published in these fields:
- Artificial intelligence (73.54%)
- Pattern recognition (44.05%)
- Computer vision (21.84%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (73.54%)
- Pattern recognition (44.05%)
- Computer vision (21.84%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Image and Feature extraction.
His Artificial intelligence research focuses on Machine learning and how it connects with Facial recognition system.
The study incorporates disciplines such as Feature, Object detection, Representation, Manifold and Robustness in addition to Pattern recognition.
He has included themes like Basis and The Internet in his Computer vision study.
His Image study deals with Generative grammar intersecting with Theoretical computer science.
While the research belongs to areas of Feature extraction, Shuicheng Yan spends his time largely on the problem of Artificial neural network, intersecting his research to questions surrounding Deep learning.
Between 2017 and 2021, his most popular works were:
- Scale-Aware Fast R-CNN for Pedestrian Detection (333 citations)
- Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation (169 citations)
- Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm (152 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Shuicheng Yan spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Image.
His is doing research in Segmentation, Pose, Deep learning, Artificial neural network and Convolutional neural network, both of which are found in Artificial intelligence.
His biological study focuses on Discriminative model.
He has researched Feature extraction in several fields, including Feature, Pipeline, Representation, Landmark and Key.
His study on Face, Facial recognition system, Three-dimensional face recognition and Active appearance model is often connected to Streak as part of broader study in Computer vision.
Shuicheng Yan interconnects Visualization, Convolution, Translation and Injective function in the investigation of issues within Image.
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