Yu-Gang Jiang mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Convolutional neural network and Motion. He merges many fields, such as Artificial intelligence and TRECVID, in his writings. His research in Machine learning intersects with topics in Annotation, Visualization, Feature extraction and Object detection.
His work on Unsupervised learning as part of general Pattern recognition research is frequently linked to Detector, bridging the gap between disciplines. His work carried out in the field of Convolutional neural network brings together such families of science as Network architecture, Image, Closed captioning, Semantics and Natural language. The study incorporates disciplines such as Class and Representation in addition to Motion.
Yu-Gang Jiang spends much of his time researching Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Information retrieval. His Artificial intelligence study often links to related topics such as Computer vision. His research integrates issues of Training set, Object detection, Categorization, The Internet and Semantics in his study of Machine learning.
His work deals with themes such as Contextual image classification, Artificial neural network and Image retrieval, which intersect with Pattern recognition. His Deep learning research is multidisciplinary, incorporating elements of Feature and Benchmark. His Information retrieval research includes elements of Context and Similarity.
His primary areas of study are Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Embedding. His study in Representation, Deep learning, Image, Closed captioning and Sentence is done as part of Artificial intelligence. His work on Discriminative model as part of his general Machine learning study is frequently connected to Baseline, thereby bridging the divide between different branches of science.
His work on RGB color model as part of general Computer vision research is frequently linked to Pedestrian detection, thereby connecting diverse disciplines of science. His Pattern recognition research is multidisciplinary, relying on both Domain, Image retrieval, Task and Benchmark. His Embedding research incorporates themes from Sketch and Semantics.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Embedding, Task analysis and Visualization. Artificial intelligence connects with themes related to Pattern recognition in his study. As a part of the same scientific family, Yu-Gang Jiang mostly works in the field of Pattern recognition, focusing on Representation and, on occasion, Artificial neural network.
His Machine learning study combines topics in areas such as RGB color model, Object detection, Pascal and Learning object. His study on Embedding also encompasses disciplines like
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Supervised hashing with kernels
Wei Liu;Jun Wang;Rongrong Ji;Yu-Gang Jiang.
computer vision and pattern recognition (2012)
Evaluating bag-of-visual-words representations in scene classification
Jun Yang;Yu-Gang Jiang;Alexander G. Hauptmann;Chong-Wah Ngo.
multimedia information retrieval (2007)
Towards optimal bag-of-features for object categorization and semantic video retrieval
Yu-Gang Jiang;Chong-Wah Ngo;Jun Yang.
conference on image and video retrieval (2007)
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Nanyang Wang;Yinda Zhang;Zhuwen Li;Yanwei Fu.
european conference on computer vision (2018)
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Zhiqiang Shen;Zhuang Liu;Jianguo Li;Yu-Gang Jiang.
international conference on computer vision (2017)
The MediaMill TRECVID 2011 Semantic Video Search Engine
C. G. M. Snoek;K. E. A. van de Sande;X. Li;M. Mazloom.
TRECVID workshop (2011)
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
Zuxuan Wu;Xi Wang;Yu-Gang Jiang;Hao Ye.
acm multimedia (2015)
NAIS: Neural Attentive Item Similarity Model for Recommendation
Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu.
IEEE Transactions on Knowledge and Data Engineering (2018)
Consumer video understanding: a benchmark database and an evaluation of human and machine performance
Yu-Gang Jiang;Guangnan Ye;Shih-Fu Chang;Daniel Ellis.
international conference on multimedia retrieval (2011)
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
Yu-Gang Jiang;Zuxuan Wu;Jun Wang;Xiangyang Xue.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
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