What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Steven C. H. Hoi spends much of his time researching Artificial intelligence, Machine learning, Image retrieval, Kernel and Pattern recognition.
His Artificial intelligence study frequently links to adjacent areas such as Metric.
His Machine learning study combines topics in areas such as Contextual image classification, Key and Data mining.
His Image retrieval research incorporates themes from Supervised learning, Boosting and Support vector machine.
The Pattern recognition study combines topics in areas such as Feature, Categorization and Graph.
Steven C. H. Hoi has researched Content-based image retrieval in several fields, including Convolutional neural network and Relevance feedback.
His most cited work include:
- Deep Learning for Content-Based Image Retrieval: A Comprehensive Study (565 citations)
- Batch mode active learning and its application to medical image classification (319 citations)
- AR-miner: mining informative reviews for developers from mobile app marketplace (317 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Image retrieval and Data mining.
Much of his study explores Artificial intelligence relationship to Computer vision.
His work on Online machine learning, Active learning, Support vector machine and Kernel method as part of his general Machine learning study is frequently connected to Scalability, thereby bridging the divide between different branches of science.
His Image retrieval research incorporates elements of Annotation and Information retrieval.
His Training set study deals with Contextual image classification intersecting with Feature learning.
His Deep learning study incorporates themes from Object detection and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (73.14%)
- Machine learning (48.14%)
- Pattern recognition (15.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (73.14%)
- Machine learning (48.14%)
- Natural language processing (6.12%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Natural language processing, Deep learning and Benchmark.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Pascal.
His work on Supervised learning as part of general Machine learning study is frequently connected to Knowledge transfer, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
In Natural language processing, he works on issues like Code, which are connected to Embedding.
In his work, Closed captioning and Structure is strongly intertwined with Information retrieval, which is a subfield of Benchmark.
His studies examine the connections between Training set and genetics, as well as such issues in Semi-supervised learning, with regards to Mixture model and Set.
Between 2019 and 2021, his most popular works were:
- Deep Learning for Image Super-resolution: A Survey (137 citations)
- Robust Graph Learning From Noisy Data (102 citations)
- Deep Learning for Person Re-identification: A Survey and Outlook (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Machine learning, Feature learning, Deep learning and Object are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Pattern recognition, Code and Natural language processing.
The Transformer research Steven C. H. Hoi does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Economic shortage, therefore creating a link between diverse domains of science.
Steven C. H. Hoi combines subjects such as Ranking, Metric, Categorization and Data science with his study of Feature learning.
His studies deal with areas such as Feature, Baseline, Contextual image classification, Superresolution and Resolution as well as Deep learning.
His Object research is multidisciplinary, incorporating perspectives in Class and Segmentation.
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