What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Junchi Yan focuses on Artificial intelligence, Pattern recognition, Algorithm, Matching and 3-dimensional matching.
His Artificial intelligence research integrates issues from Machine learning and Computer vision.
Junchi Yan combines subjects such as Global matching, Autoencoder, Structure learning and Re identification with his study of Pattern recognition.
Junchi Yan has researched Algorithm in several fields, including Hypergraph and Mathematical optimization.
His studies deal with areas such as Theoretical computer science, Heuristic and Graph as well as Matching.
His 3-dimensional matching course of study focuses on Pairwise comparison and Optimal matching.
His most cited work include:
- Unsupervised Deep Learning for Optical Flow Estimation. (163 citations)
- Visual Saliency Detection via Sparsity Pursuit (158 citations)
- Person Re-Identification with Correspondence Structure Learning (113 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Algorithm.
His Feature, Deep learning, Embedding, Matching and Cluster analysis study are his primary interests in Artificial intelligence.
Junchi Yan has included themes like Quadratic assignment problem, Pairwise comparison and Pattern matching in his Matching study.
Junchi Yan combines topics linked to Image with his work on Pattern recognition.
His Machine learning study frequently links to related topics such as Data mining.
His Algorithm study combines topics in areas such as Artificial neural network and Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (54.61%)
- Pattern recognition (22.51%)
- Computer vision (15.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (54.61%)
- Algorithm (14.39%)
- Theoretical computer science (12.92%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Theoretical computer science, Pattern recognition and Embedding.
Junchi Yan interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence.
His studies deal with areas such as Artificial neural network, Recurrent neural network, Weighting and Convolutional neural network as well as Algorithm.
He has researched Theoretical computer science in several fields, including Matching, Iterative method and Combinatorial optimization problem.
The Pattern recognition study combines topics in areas such as Motion, Noise, Identifiability, Benchmark and Optical flow.
His study explores the link between Embedding and topics such as Node that cross with problems in Vertex and Scalability.
Between 2019 and 2021, his most popular works were:
- Image Matching from Handcrafted to Deep Features: A Survey (37 citations)
- Arbitrary-Oriented Object Detection with Circular Smooth Label (19 citations)
- Learning deep graph matching with channel-independent embedding and Hungarian attention (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Theoretical computer science, Matching, Pattern recognition and Deep learning.
His work carried out in the field of Artificial intelligence brings together such families of science as Structure and Machine learning.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Embedding and Combinatorial optimization problem.
His work is dedicated to discovering how Matching, Graph are connected with Pattern matching and Assignment problem and other disciplines.
His Pattern recognition research focuses on Optical flow and how it relates to Unsupervised learning, Motion and Variation.
His Deep learning research is multidisciplinary, incorporating perspectives in Information bottleneck method, Inpainting, Robustness and Image processing.
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