What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algebra
His primary areas of investigation include Artificial intelligence, Machine learning, Discriminative model, Pattern recognition and Representation.
Convolutional neural network, Deep learning, Training set, Visualization and Feature are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
As part of the same scientific family, Xiatian Zhu usually focuses on Deep learning, concentrating on Unsupervised learning and intersecting with Supervised learning, Usability and Feature.
His research investigates the connection between Machine learning and topics such as Variety that intersect with issues in Knowledge extraction, Contextual image classification and Network model.
His Discriminative model research incorporates themes from Fuzzy clustering, Spectral clustering, Clustering high-dimensional data, Cluster analysis and Correlation clustering.
His work carried out in the field of Pattern recognition brings together such families of science as CURE data clustering algorithm, Ranking, Data mining and Computer vision.
His most cited work include:
- Harmonious Attention Network for Person Re-identification (458 citations)
- Person re-identification by video ranking (344 citations)
- Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification (328 citations)
What are the main themes of his work throughout his whole career to date?
Xiatian Zhu mainly investigates Artificial intelligence, Machine learning, Deep learning, Training set and Discriminative model.
Artificial intelligence is closely attributed to Pattern recognition in his work.
His work in the fields of Machine learning, such as Convolutional neural network, Supervised learning and Feature, overlaps with other areas such as Scalability.
The study incorporates disciplines such as Object, Artificial neural network and Inference in addition to Deep learning.
As a member of one scientific family, Xiatian Zhu mostly works in the field of Training set, focusing on Re identification and, on occasion, Subspace topology, Leverage and Visualization.
Xiatian Zhu has researched Discriminative model in several fields, including Pyramid, Feature, Pyramid, Feature selection and Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (93.58%)
- Machine learning (63.30%)
- Deep learning (36.70%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (93.58%)
- Machine learning (63.30%)
- Unsupervised learning (14.68%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Unsupervised learning, Pattern recognition and Identity.
His biological study deals with issues like Computer vision, which deal with fields such as Representation.
His Machine learning research focuses on Deep learning in particular.
Xiatian Zhu works mostly in the field of Deep learning, limiting it down to concerns involving Discriminative model and, occasionally, Convolutional neural network and Text mining.
Xiatian Zhu combines subjects such as Visualization, Partition and Cluster analysis with his study of Unsupervised learning.
In his study, which falls under the umbrella issue of Pattern recognition, Pose is strongly linked to Image.
Between 2019 and 2021, his most popular works were:
- Unsupervised Tracklet Person Re-Identification (42 citations)
- Distribution-Aware Coordinate Representation for Human Pose Estimation (41 citations)
- Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algebra
His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Unsupervised learning and Detector.
His study in Contextual image classification, Training set, Pascal, Feature extraction and Pose falls under the purview of Artificial intelligence.
Xiatian Zhu has included themes like Text mining, Deep learning and Discriminative model in his Training set study.
His Pose research integrates issues from Image and Representation.
In the field of Machine learning, his study on Semantic clustering overlaps with subjects such as Stochastic approximation.
His research in Unsupervised learning intersects with topics in Visualization, Partition and Cluster analysis.
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