What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Changsheng Xu spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Video tracking and Feature extraction.
His Artificial intelligence research includes elements of Time complexity and Machine learning.
His work investigates the relationship between Computer vision and topics such as Identification that intersect with problems in Viterbi algorithm.
His Pattern recognition study incorporates themes from Facial recognition system, Face, Feature and Subspace topology.
His Video tracking research is multidisciplinary, incorporating perspectives in Image processing, Object detection, Multimedia and Automatic summarization.
The various areas that Changsheng Xu examines in his Feature extraction study include Contextual image classification, Discriminative model, Music information retrieval and Cluster analysis.
His most cited work include:
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
- The sixth visual object tracking VOT2018 challenge results (299 citations)
- The Visual Object Tracking VOT2017 Challenge Results (285 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, Machine learning and Feature extraction.
His Artificial intelligence study often links to related topics such as Natural language processing.
In his study, Information retrieval is strongly linked to Visualization, which falls under the umbrella field of Pattern recognition.
His work carried out in the field of Machine learning brings together such families of science as Classifier and Social media.
His research investigates the connection between Feature extraction and topics such as Speech recognition that intersect with issues in Audio signal processing and Automatic summarization.
His Video tracking study deals with Multimedia intersecting with Personalization.
He most often published in these fields:
- Artificial intelligence (59.83%)
- Pattern recognition (25.05%)
- Computer vision (20.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (59.83%)
- Pattern recognition (25.05%)
- Representation (7.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Representation, Natural language processing and Image.
As a member of one scientific family, he mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Graph neural networks.
His biological study spans a wide range of topics, including Visualization, Feature and Joint.
His Representation study integrates concerns from other disciplines, such as Embedding and Information retrieval.
His study in Natural language processing is interdisciplinary in nature, drawing from both Adversarial system, The Internet and Search engine.
His Image research incorporates elements of Task, Word and Human–computer interaction.
Between 2019 and 2021, his most popular works were:
- Dynamic Refinement Network for Oriented and Densely Packed Object Detection (17 citations)
- Geometry Guided Pose-Invariant Facial Expression Recognition (9 citations)
- Density-Aware Multi-Task Learning for Crowd Counting (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Semantics, Feature extraction and Deep learning.
In general Artificial intelligence study, his work on Visualization often relates to the realm of Task analysis, thereby connecting several areas of interest.
His Pattern recognition research incorporates themes from Contextual image classification and Benchmark.
His work in Semantics tackles topics such as Information retrieval which are related to areas like User modeling.
His work deals with themes such as Detector, Feature, Object detection and Facial recognition system, Face, which intersect with Feature extraction.
His Deep learning research integrates issues from Facial expression recognition, Image synthesis, Training set and Geometry, Invariant.
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