What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Feature extraction and Information retrieval.
As part of his studies on Artificial intelligence, Richang Hong often connects relevant areas like Computer vision.
His Machine learning research is multidisciplinary, incorporating elements of Training set and Social network.
His Pattern recognition research includes themes of Contextual image classification and Automatic image annotation.
As part of the same scientific family, Richang Hong usually focuses on Feature extraction, concentrating on Visualization and intersecting with Semantic gap, Motion and Data science.
His biological study spans a wide range of topics, including Ranking, Storyboard and Image retrieval.
His most cited work include:
- NUS-WIDE: a real-world web image database from National University of Singapore (1753 citations)
- Unified Video Annotation via Multigraph Learning (412 citations)
- Crowded Scene Analysis: A Survey (307 citations)
What are the main themes of his work throughout his whole career to date?
Richang Hong mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Information retrieval.
Artificial intelligence is a component of his Discriminative model, Feature extraction, Image, Feature and Convolutional neural network studies.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Contextual image classification, Subspace topology, Cluster analysis and Robustness.
Richang Hong works mostly in the field of Machine learning, limiting it down to topics relating to Social network and, in certain cases, Collaborative filtering.
His research integrates issues of Salient and Metric in his study of Computer vision.
His Information retrieval study integrates concerns from other disciplines, such as Ranking, World Wide Web and Image retrieval.
He most often published in these fields:
- Artificial intelligence (62.69%)
- Pattern recognition (25.00%)
- Machine learning (24.23%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (62.69%)
- Machine learning (24.23%)
- Embedding (7.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Embedding, Recommender system and Feature learning.
The various areas that Richang Hong examines in his Artificial intelligence study include Computer vision and Social network.
Richang Hong interconnects Context, Similarity, Inference and Space in the investigation of issues within Machine learning.
His Embedding research incorporates themes from Transfer of learning, Theoretical computer science, Control theory and Training set.
His Recommender system research entails a greater understanding of Information retrieval.
The study incorporates disciplines such as Discriminative model, Word embedding and Identification in addition to Feature learning.
Between 2019 and 2021, his most popular works were:
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (46 citations)
- Exploiting Subspace Relation in Semantic Labels for Cross-modal Hashing (29 citations)
- Cross-Entropy Adversarial View Adaptation for Person Re-Identification (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Richang Hong mainly focuses on Artificial intelligence, Recommender system, Machine learning, Theoretical computer science and Embedding.
His Artificial intelligence research is multidisciplinary, relying on both Pattern recognition and Social network.
His study explores the link between Pattern recognition and topics such as Metric that cross with problems in Subspace topology.
Recommender system is a subfield of Information retrieval that Richang Hong investigates.
Richang Hong is interested in Collaborative filtering, which is a field of Machine learning.
His work in Feature tackles topics such as Image quality which are related to areas like Feature extraction.
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