What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Feature and Sparse approximation.
His work in the fields of Feature extraction, Feature detection and Deep learning overlaps with other areas such as Adaptation and Generalization.
His research integrates issues of Object, Kadir–Brady saliency detector, Motion and Conditional random field in his study of Feature extraction.
Yu-Chiang Frank Wang has included themes like Facial recognition system, Iterative reconstruction, Computer vision and Robustness in his Pattern recognition study.
His research in Machine learning intersects with topics in Contextual image classification, Subspace topology and Classifier.
His Statistical classification study combines topics in areas such as Variation, Classifier and Shot.
His most cited work include:
- A Closer Look at Few-shot Classification. (326 citations)
- Low-rank matrix recovery with structural incoherence for robust face recognition (188 citations)
- No More Discrimination: Cross City Adaptation of Road Scene Segmenters (186 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, Computer vision, Machine learning and Image.
His work is connected to Deep learning, Robustness, Sparse approximation, Feature and Feature extraction, as a part of Artificial intelligence.
His studies in Feature extraction integrate themes in fields like Video tracking and Visualization.
The various areas that Yu-Chiang Frank Wang examines in his Pattern recognition study include Contextual image classification, Facial recognition system and Visual Word.
Within one scientific family, Yu-Chiang Frank Wang focuses on topics pertaining to Training set under Machine learning, and may sometimes address concerns connected to Discriminative model.
His Image research integrates issues from Iterative reconstruction, Task and Benchmark.
He most often published in these fields:
- Artificial intelligence (90.75%)
- Pattern recognition (50.87%)
- Computer vision (37.57%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.75%)
- Machine learning (27.75%)
- Image (17.92%)
In recent papers he was focusing on the following fields of study:
Yu-Chiang Frank Wang spends much of his time researching Artificial intelligence, Machine learning, Image, Visualization and Robustness.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Pattern recognition.
His studies examine the connections between Pattern recognition and genetics, as well as such issues in Interpolation, with regards to MNIST database, Feature learning, Image warping, Feature and Autoencoder.
He interconnects Exploit and Training set in the investigation of issues within Machine learning.
His work carried out in the field of Visualization brings together such families of science as Speech recognition, Inpainting, Consistency, Ground truth and Feature extraction.
His work in Robustness addresses issues such as Task, which are connected to fields such as Content.
Between 2019 and 2021, his most popular works were:
- Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis (14 citations)
- Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond. (7 citations)
- A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Machine learning, Deep learning, Image translation and Segmentation.
Yu-Chiang Frank Wang combines topics linked to Audio visual with his work on Artificial intelligence.
His work on Re identification is typically connected to Matching as part of general Machine learning study, connecting several disciplines of science.
His study focuses on the intersection of Segmentation and fields such as Data point with connections in the field of Feature extraction and Kernel.
He has included themes like Supervised learning, Similarity and Image retrieval in his Representation study.
His Robustness study incorporates themes from Network architecture, Image manipulation, External Data Representation and Data domain.
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