What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
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 most cited work include:
- Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (360 citations)
- Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification (232 citations)
- Joint multi-label multi-instance learning for image classification (207 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (71.77%)
- Pattern recognition (33.03%)
- Computer vision (16.52%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (71.77%)
- Pattern recognition (33.03%)
- Discriminative model (12.01%)
In recent papers he was focusing on the following fields of study:
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
- Feature learning which connect with Cluster analysis, Identity and Image restoration,
- Embedding which is related to area like Margin, Information retrieval, Semantics and Modality.
Within one scientific family, Zheng-Jun Zha focuses on topics pertaining to Relation under Representation, and may sometimes address concerns connected to Metric.
His Feature research incorporates themes from Representation, Salient and Variation.
Between 2019 and 2021, his most popular works were:
- Context-Aware Visual Policy Network for Fine-Grained Image Captioning. (32 citations)
- Adversarial Attribute-Text Embedding for Person Search With Natural Language Query (23 citations)
- Object Relational Graph With Teacher-Recommended Learning for Video Captioning (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
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.
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