What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Image retrieval, Information retrieval, Multimedia and Pattern recognition.
Yong Rui combines subjects such as Machine learning and Computer vision with his study of Artificial intelligence.
His work on Visual Word as part of his general Image retrieval study is frequently connected to Hypergraph, thereby bridging the divide between different branches of science.
His studies in Information retrieval integrate themes in fields like Ranking, Image processing and Relevance feedback.
His Multimedia study combines topics in areas such as Uncompressed video, Video processing, Smacker video, Microphone array and Video compression picture types.
His research in the fields of Nonlinear dimensionality reduction, Discriminative model and Feature extraction overlaps with other disciplines such as Graph theory.
His most cited work include:
- Relevance feedback: a power tool for interactive content-based image retrieval (1652 citations)
- Content-based image retrieval with relevance feedback in MARS (690 citations)
- system and method for distributed meetings (478 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Multimedia, Computer vision, Information retrieval and Pattern recognition.
In his study, TRECVID and Data mining is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence.
His research in Multimedia intersects with topics in Session, World Wide Web, Table of contents, Video browsing and Automatic summarization.
His research on Information retrieval often connects related areas such as Ranking.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification and Matching.
Image retrieval and Feature are frequently intertwined in his study.
He most often published in these fields:
- Artificial intelligence (44.15%)
- Multimedia (24.75%)
- Computer vision (19.73%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (44.15%)
- Computer vision (19.73%)
- Information retrieval (18.39%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Information retrieval, Convolutional neural network and Pattern recognition are his primary areas of study.
His research combines Machine learning and Artificial intelligence.
His research integrates issues of Layer and Background noise in his study of Computer vision.
His work on Automatic summarization and Collaborative filtering as part of general Information retrieval research is frequently linked to Scalability, bridging the gap between disciplines.
His Convolutional neural network research integrates issues from Object, Representation, Margin and Set.
As part of the same scientific family, Yong Rui usually focuses on Deep learning, concentrating on Multimedia and intersecting with Graphics and Artificial neural network.
Between 2015 and 2020, his most popular works were:
- MSR-VTT: A Large Video Description Dataset for Bridging Video and Language (458 citations)
- Jointly Modeling Embedding and Translation to Bridge Video and Language (413 citations)
- Hierarchical Deep Click Feature Prediction for Fine-grained Image Recognition. (162 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yong Rui mostly deals with Artificial intelligence, Information retrieval, Pattern recognition, Computer vision and Electronic mail.
As a member of one scientific family, Yong Rui mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Task.
His Information retrieval research is multidisciplinary, incorporating perspectives in Metadata and Preference learning.
Yong Rui works in the field of Pattern recognition, namely Feature extraction.
The Feature extraction study combines topics in areas such as Modality, Feature, Autoencoder and Representation.
The concepts of his Video tracking study are interwoven with issues in Image segmentation and Video processing.
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