Zheng-Jun Zha spends much of his time researching Artificial intelligence, Information retrieval, Machine learning, Pattern recognition and The Internet. His research on Artificial intelligence frequently connects to adjacent areas such as Data mining. In general Machine learning study, his work on Semi-supervised learning often relates to the realm of TRECVID and Sample, thereby connecting several areas of interest.
His work on Support vector machine as part of general Pattern recognition research is frequently linked to Video quality, bridging the gap between disciplines. His The Internet research includes themes of Question answering, Text mining and Automatic summarization. His studies deal with areas such as Cluster analysis and Robustness as well as Feature extraction.
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Information retrieval. His study in Feature, Discriminative model, Convolutional neural network, Feature extraction and Image is done as part of Artificial intelligence. His study focuses on the intersection of Pattern recognition and fields such as Representation with connections in the field of Natural language processing.
In the subject of general Machine learning, his work in Semi-supervised learning is often linked to TRECVID, thereby combining diverse domains of study. His Information retrieval research integrates issues from The Internet and Image retrieval. His Query expansion research incorporates elements of Web search query and Web query classification.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Discriminative model, Representation and Feature. His Artificial intelligence study incorporates themes from Machine learning, Computer vision and Natural language processing. His work in the fields of Pattern recognition, such as Segmentation and Convolutional neural network, intersects with other areas such as Code.
His Discriminative model research also works with subjects such as
The scientist’s investigation covers issues in Artificial intelligence, Feature extraction, Pattern recognition, Discriminative model and Visualization. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Natural language processing. His research integrates issues of Structure, Normalization and Interpretability in his study of Feature extraction.
His Pattern recognition research is multidisciplinary, relying on both False positive paradox and Scale. In his work, Semantics, Margin, Parsing and Pooling is strongly intertwined with Embedding, which is a subfield of Discriminative model. The study incorporates disciplines such as Entropy, Segmentation, Zero shot learning and Softmax function in addition to Visualization.
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.
Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search
Yue Gao;Meng Wang;Zheng-Jun Zha;Jialie Shen.
IEEE Transactions on Image Processing (2013)
Joint multi-label multi-instance learning for image classification
Zheng-Jun Zha;Xian-Sheng Hua;Tao Mei;Jingdong Wang.
computer vision and pattern recognition (2008)
Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews
Jianxing Yu;Zheng-Jun Zha;Meng Wang;Tat-Seng Chua.
meeting of the association for computational linguistics (2011)
Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification
Meng Wang;R. Hong;Guangda Li;Zheng-Jun Zha.
IEEE Transactions on Multimedia (2012)
Visual query suggestion
Zheng-Jun Zha;Linjun Yang;Tao Mei;Meng Wang.
acm multimedia (2009)
Less is More: Efficient 3-D Object Retrieval With Query View Selection
Yue Gao;Meng Wang;Zheng-Jun Zha;Qi Tian.
IEEE Transactions on Multimedia (2011)
Mining Travel Patterns from Geotagged Photos
Yan-Tao Zheng;Zheng-Jun Zha;Tat-Seng Chua.
ACM Transactions on Intelligent Systems and Technology (2012)
Graph-based semi-supervised learning with multiple labels
Zheng-Jun Zha;Tao Mei;Jingdong Wang;Zengfu Wang.
Journal of Visual Communication and Image Representation (2009)
Multi-Scale Triplet CNN for Person Re-Identification
Jiawei Liu;Zheng-Jun Zha;Qi Tian;Dong Liu.
acm multimedia (2016)
A Fast Uyghur Text Detector for Complex Background Images
Chenggang Yan;Hongtao Xie;Jianjun Chen;Zhengjun Zha.
IEEE Transactions on Multimedia (2018)
Profile was last updated on December 6th, 2021.
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