What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Chong-Wah Ngo mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Visual Word and Information retrieval.
His research in Artificial intelligence intersects with topics in Machine learning and TRECVID.
The concepts of his Pattern recognition study are interwoven with issues in Scalability, Similarity, Automatic summarization and Pattern matching.
In his research on the topic of Computer vision, Similarity measure is strongly related with Cluster analysis.
His Visual Word study incorporates themes from Feature extraction and Vocabulary.
The Information retrieval study combines topics in areas such as Video tracking and Social media.
His most cited work include:
- Evaluating bag-of-visual-words representations in scene classification (675 citations)
- Towards optimal bag-of-features for object categorization and semantic video retrieval (590 citations)
- Video summarization and scene detection by graph modeling (309 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Information retrieval, Computer vision, Pattern recognition and TRECVID are his primary areas of study.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Natural language processing.
His Machine learning research is multidisciplinary, relying on both Image and Data mining.
Chong-Wah Ngo has researched Information retrieval in several fields, including World Wide Web, The Internet and Image retrieval.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Matching, Similarity and Feature.
Chong-Wah Ngo interconnects Event, Multimedia and Speech recognition in the investigation of issues within TRECVID.
He most often published in these fields:
- Artificial intelligence (50.53%)
- Information retrieval (30.60%)
- Computer vision (20.28%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (50.53%)
- Information retrieval (30.60%)
- TRECVID (17.08%)
In recent papers he was focusing on the following fields of study:
Chong-Wah Ngo focuses on Artificial intelligence, Information retrieval, TRECVID, Modal and Machine learning.
His Artificial intelligence study frequently links to adjacent areas such as Pattern recognition.
His Pattern recognition study integrates concerns from other disciplines, such as Domain adaptation, MNIST database, Feature and Adaptation.
His Information retrieval research is multidisciplinary, incorporating elements of Hyperlink, Event and Structure.
His studies in TRECVID integrate themes in fields like Speech recognition, Multimedia, Vocabulary, Semantic gap and Natural language.
His biological study spans a wide range of topics, including Ranking and Image segmentation.
Between 2015 and 2021, his most popular works were:
- Deep-based Ingredient Recognition for Cooking Recipe Retrieval (151 citations)
- Transferrable Prototypical Networks for Unsupervised Domain Adaptation (93 citations)
- Learning Spatio-Temporal Representation With Local and Global Diffusion (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Information retrieval, Recipe, Categorization and Modal.
His works in Embedding, Feature learning, Deep learning, Domain knowledge and Feature extraction are all subjects of inquiry into Artificial intelligence.
He has included themes like Class, Pattern recognition, Adaptation, Domain adaptation and MNIST database in his Embedding study.
In his work, Visualization is strongly intertwined with Manifold, which is a subfield of Feature extraction.
The study incorporates disciplines such as Semantics, Event, Relation and TRECVID in addition to Information retrieval.
His work focuses on many connections between Categorization and other disciplines, such as Machine learning, that overlap with his field of interest in Real image and Rendering.
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