What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Information retrieval, Machine learning, Relevance and Computer vision are his primary areas of study.
His Artificial intelligence study incorporates themes from Data mining and Pattern recognition.
His Information retrieval study combines topics in areas such as Feature, Annotation and Image retrieval, Automatic image annotation, Visual Word.
His work on Support vector machine as part of general Machine learning research is often related to TRECVID, thus linking different fields of science.
The concepts of his Relevance study are interwoven with issues in Multimedia and The Internet.
His work focuses on many connections between Computer vision and other disciplines, such as Computer graphics, that overlap with his field of interest in Multiview Video Coding, Motion compensation and Video post-processing.
His most cited work include:
- Automatic video recommendation (132 citations)
- Automatic detection and segmentation of music videos in an audio/video stream (127 citations)
- Online video recommendation based on multimodal fusion and relevance feedback (118 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Information retrieval, Machine learning, Pattern recognition and Multimedia.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer vision.
Annotation is closely connected to Automatic image annotation in his research, which is encompassed under the umbrella topic of Information retrieval.
His Machine learning research integrates issues from Classifier, Training set, Data mining and Video annotation.
The study incorporates disciplines such as Contextual image classification, Kernel and Cluster analysis in addition to Pattern recognition.
His Multimedia research is multidisciplinary, incorporating perspectives in Video content analysis, World Wide Web, The Internet, Video processing and Mobile computing.
He most often published in these fields:
- Artificial intelligence (53.62%)
- Information retrieval (29.61%)
- Machine learning (25.00%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (53.62%)
- Information retrieval (29.61%)
- Machine learning (25.00%)
In recent papers he was focusing on the following fields of study:
Xian-Sheng Hua focuses on Artificial intelligence, Information retrieval, Machine learning, World Wide Web and Pattern recognition.
Xian-Sheng Hua combines topics linked to Computer vision with his work on Artificial intelligence.
His study in the field of Relevance and Ranking is also linked to topics like TRECVID.
His work in the fields of Machine learning, such as Margin, overlaps with other areas such as Identity.
While the research belongs to areas of World Wide Web, Xian-Sheng Hua spends his time largely on the problem of Variety, intersecting his research to questions surrounding Semantic search, Semantic gap, Data science and Visualization.
His biological study spans a wide range of topics, including Space partitioning and Ternary search tree.
Between 2012 and 2021, his most popular works were:
- Clickage: towards bridging semantic and intent gaps via mining click logs of search engines (93 citations)
- Social image tagging with diverse semantics. (45 citations)
- Trinary-Projection Trees for Approximate Nearest Neighbor Search (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Information retrieval, Pattern recognition, Semantics and Automatic summarization.
In general Artificial intelligence study, his work on Language bias, Image and Question answering often relates to the realm of Baseline and Causal inference, thereby connecting several areas of interest.
His Information retrieval research incorporates themes from Tag cloud and Data mining.
His Spanning tree research extends to the thematically linked field of Pattern recognition.
He has researched Semantics in several fields, including Social media, Leverage and Upload.
His Automatic summarization research incorporates elements of Video tracking, Video compression picture types, Multimedia, Data compression and Video processing.
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