What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Junsong Yuan mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Discriminative model.
In his study, Branch and bound is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence.
His study explores the link between Computer vision and topics such as Detector that cross with problems in Dynamic programming and Scale.
His work carried out in the field of Pattern recognition brings together such families of science as Feature, Image and Selection.
His Feature extraction research is multidisciplinary, incorporating perspectives in Salient, Data mining, Curse of dimensionality, Hidden Markov model and Phrase.
His Discriminative model study combines topics from a wide range of disciplines, such as Object, Cognitive neuroscience of visual object recognition, Spectral clustering, Categorization and Boosting.
His most cited work include:
- Mining actionlet ensemble for action recognition with depth cameras (1170 citations)
- Sparse reconstruction cost for abnormal event detection (552 citations)
- Robust Part-Based Hand Gesture Recognition Using Kinect Sensor (491 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Machine learning.
His study involves Feature extraction, Object, Pose, Object detection and Segmentation, a branch of Artificial intelligence.
Feature extraction is closely attributed to Visualization in his research.
His Pattern recognition study incorporates themes from Pixel, Feature and Benchmark.
His Robustness research extends to Computer vision, which is thematically connected.
His Discriminative model research includes elements of Mutual information and Leverage.
He most often published in these fields:
- Artificial intelligence (82.32%)
- Pattern recognition (43.27%)
- Computer vision (36.94%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.32%)
- Pattern recognition (43.27%)
- Computer vision (36.94%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object are his primary areas of study.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pose, Discriminative model, Convolutional neural network, Leverage and RGB color model.
His research integrates issues of Object detection, Autoencoder and Benchmark in his study of Pattern recognition.
The study incorporates disciplines such as Frame and Reliability in addition to Computer vision.
His Categorical variable study, which is part of a larger body of work in Machine learning, is frequently linked to Selection, bridging the gap between disciplines.
His studies in Object integrate themes in fields like Margin, Construct and Segmentation.
Between 2019 and 2021, his most popular works were:
- Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians (13 citations)
- Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction (9 citations)
- Learning progressive joint propagation for human motion prediction (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Junsong Yuan mainly investigates Artificial intelligence, Pattern recognition, Computer vision, RGB color model and Machine learning.
By researching both Artificial intelligence and Selection, he produces research that crosses academic boundaries.
His work in the fields of Feature learning, Convolutional neural network and Anomaly detection overlaps with other areas such as Action recognition.
His biological study deals with issues like Information extraction, which deal with fields such as Discriminative model and Benchmark.
His work on Feature and Human motion as part of general Computer vision study is frequently linked to Pedestrian detection and Joint, therefore connecting diverse disciplines of science.
His Machine learning research incorporates themes from Focus and Sequence.
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