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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)
The Visual Object Tracking VOT2017 Challenge Results
Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg.
international conference on computer vision (2017)
Street-to-shop: cross-scenario clothing retrieval via parts alignment and auxiliary set
Si Liu;Zheng Song;Meng Wang;Changsheng Xu.
acm multimedia (2012)
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set
Si Liu;Zheng Song;Guangcan Liu;Changsheng Xu.
computer vision and pattern recognition (2012)
The sixth visual object tracking VOT2018 challenge results
Matej Kristan;Aleš Leonardis;Jiří Matas;Michael Felsberg.
european conference on computer vision (2019)
Multi-task Correlation Particle Filter for Robust Object Tracking
Tianzhu Zhang;Changsheng Xu;Ming-Hsuan Yang.
computer vision and pattern recognition (2017)
Hi, magic closet, tell me what to wear!
Si Liu;Jiashi Feng;Zheng Song;Tianzhu Zhang.
acm multimedia (2012)
A mid-level representation framework for semantic sports video analysis
Ling-Yu Duan;Min Xu;Tat-Seng Chua;Qi Tian.
acm multimedia (2003)
A unified framework for semantic shot classification in sports video
Ling-Yu Duan;Min Xu;Qi Tian;Chang-Sheng Xu.
IEEE Transactions on Multimedia (2005)
Deep Representation Learning With Part Loss for Person Re-Identification
Hantao Yao;Shiliang Zhang;Richang Hong;Yongdong Zhang.
IEEE Transactions on Image Processing (2019)
Profile was last updated on December 6th, 2021.
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