Yongdong Zhang focuses on Artificial intelligence, Computer vision, Pattern recognition, Data mining and Feature extraction. His research combines Machine learning and Artificial intelligence. His Computer vision study combines topics in areas such as Artificial neural network and Time complexity.
The study incorporates disciplines such as Receptive field, Parsing, Convolution, Differentiable function and Kernel in addition to Pattern recognition. In general Data mining, his work in Identification is often linked to mHealth linking many areas of study. As part of the same scientific family, Yongdong Zhang usually focuses on Feature extraction, concentrating on Feature and intersecting with Joint and Representation.
Yongdong Zhang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction. Feature, Image retrieval, Image, Convolutional neural network and Segmentation are the primary areas of interest in his Artificial intelligence study. Yongdong Zhang has included themes like Cluster analysis and Robustness in his Pattern recognition study.
His research on Computer vision often connects related areas such as Frame. Machine learning is frequently linked to Data mining in his study. Information retrieval is closely connected to Visualization in his research, which is encompassed under the umbrella topic of Feature extraction.
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Image and Discriminative model. His Computer vision research extends to Artificial intelligence, which is thematically connected. His work carried out in the field of Pattern recognition brings together such families of science as Pixel, Deep learning, Pooling and Similarity.
Yongdong Zhang has researched Machine learning in several fields, including Semantics and Reliability. The Image study combines topics in areas such as Translation, Relation, Information retrieval, Algorithm and Key. His study focuses on the intersection of Discriminative model and fields such as Feature learning with connections in the field of Training set and Feature.
His scientific interests lie mostly in Artificial intelligence, Closed captioning, Pattern recognition, Artificial neural network and Convolutional neural network. His Artificial intelligence research includes elements of Machine learning, Computer vision and Natural language processing. His Computer vision research is multidisciplinary, relying on both Deep learning and Boundary.
His studies in Closed captioning integrate themes in fields like Speech recognition and Natural language. The Pattern recognition study combines topics in areas such as Consistency and Scale. His work deals with themes such as Character, Computer-aided diagnosis, Discriminative model and Sequence, which intersect with Convolutional neural network.
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Deep Learning for Content-Based Image Retrieval: A Comprehensive Study
Ji Wan;Dayong Wang;Steven Chu Hong Hoi;Pengcheng Wu.
acm multimedia (2014)
A density-based method for adaptive LDA model selection
Juan Cao;Tian Xia;Jintao Li;Yongdong Zhang.
Multiview Spectral Embedding
Tian Xia;Dacheng Tao;Tao Mei;Yongdong Zhang.
systems man and cybernetics (2010)
Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors
Chenggang Clarence Yan;Yongdong Zhang;Jizheng Xu;Feng Dai.
IEEE Transactions on Circuits and Systems for Video Technology (2014)
A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors
Chenggang Yan;Yongdong Zhang;Jizheng Xu;Feng Dai.
IEEE Signal Processing Letters (2014)
Drug–target interaction prediction: databases, web servers and computational models
Xing Chen;Chenggang Clarence Yan;Xiaotian Zhang;Xu Zhang.
Briefings in Bioinformatics (2016)
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.
Xing Chen;Chenggang Clarence Yan;Chenggang Clarence Yan;Xu Zhang;Zhu-Hong You.
Scientific Reports (2016)
Deep Representation Learning With Part Loss for Person Re-Identification
Hantao Yao;Shiliang Zhang;Richang Hong;Yongdong Zhang.
IEEE Transactions on Image Processing (2019)
Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles
Chenggang Yan;Hongtao Xie;Dongbao Yang;Jian Yin.
IEEE Transactions on Intelligent Transportation Systems (2018)
Multi-task deep visual-semantic embedding for video thumbnail selection
Wu Liu;Tao Mei;Yongdong Zhang;Cherry Che.
computer vision and pattern recognition (2015)
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