H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 52 Citations 9,033 270 World Ranking 2623 National Ranking 251

Overview

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.

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.

Top Publications

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)

409 Citations

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)

289 Citations

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)

286 Citations

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)

257 Citations

Visual query suggestion

Zheng-Jun Zha;Linjun Yang;Tao Mei;Meng Wang.
acm multimedia (2009)

216 Citations

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)

212 Citations

Mining Travel Patterns from Geotagged Photos

Yan-Tao Zheng;Zheng-Jun Zha;Tat-Seng Chua.
ACM Transactions on Intelligent Systems and Technology (2012)

211 Citations

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)

208 Citations

Multi-Scale Triplet CNN for Person Re-Identification

Jiawei Liu;Zheng-Jun Zha;Qi Tian;Dong Liu.
acm multimedia (2016)

167 Citations

A Fast Uyghur Text Detector for Complex Background Images

Chenggang Yan;Hongtao Xie;Jianjun Chen;Zhengjun Zha.
IEEE Transactions on Multimedia (2018)

160 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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