What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Image.
His study involves Feature extraction, Discriminative model, Deep learning, Contextual image classification and Feature, a branch of Artificial intelligence.
The study incorporates disciplines such as Pixel, Speech recognition, Text mining, Data compression and Search engine indexing in addition to Pattern recognition.
The various areas that Yonghong Tian examines in his Machine learning study include Kernel and Pattern recognition.
His work on Channel, Image enhancement and Context-adaptive binary arithmetic coding as part of general Computer vision study is frequently linked to Underwater, therefore connecting diverse disciplines of science.
When carried out as part of a general Image research project, his work on Image quality is frequently linked to work in Quality, Focus and Visibility, therefore connecting diverse disciplines of study.
His most cited work include:
- Deep Relative Distance Learning: Tell the Difference between Similar Vehicles (320 citations)
- Unsupervised Cross-Dataset Transfer Learning for Person Re-identification (230 citations)
- Deep Transfer Learning for Person Re-identification (160 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction.
All of his Artificial intelligence and Discriminative model, Object detection, Image, Feature and Deep learning investigations are sub-components of the entire Artificial intelligence study.
His work deals with themes such as Frame and Visualization, which intersect with Computer vision.
His research ties Retina and Visualization together.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Artificial neural network, Salient, Kernel and Cluster analysis.
His Machine learning research includes themes of Contextual image classification, Inference and Benchmark.
He most often published in these fields:
- Artificial intelligence (73.58%)
- Computer vision (31.44%)
- Pattern recognition (26.09%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (73.58%)
- Machine learning (20.74%)
- Pattern recognition (26.09%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Artificial neural network.
His study in Deep learning, Discriminative model, Feature, Benchmark and Representation is done as part of Artificial intelligence.
He has included themes like Contextual image classification, Object and Construct in his Machine learning study.
His research in Pattern recognition intersects with topics in Object detection and Cluster analysis.
Yonghong Tian combines subjects such as Frame and Visualization with his study of Computer vision.
His biological study spans a wide range of topics, including Retina, Receptive field, Convolutional neural network and Pruning.
Between 2019 and 2021, his most popular works were:
- HRank: Filter Pruning Using High-Rank Feature Map (72 citations)
- AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification (33 citations)
- Channel Pruning via Automatic Structure Search (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Pattern recognition, Machine learning, Regularization and Computer vision are his primary areas of study.
His Artificial intelligence study frequently draws connections to other fields, such as Adaptation.
In the subject of general Pattern recognition, his work in Feature vector is often linked to Test data, thereby combining diverse domains of study.
As a part of the same scientific study, Yonghong Tian usually deals with the Machine learning, concentrating on Benchmark and frequently concerns with Representation.
His work carried out in the field of Computer vision brings together such families of science as Visualization, Plug and play and Forgetting.
His Feature research includes elements of Artificial neural network and Feature extraction.
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