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, Computer vision, Pattern recognition, Machine learning and Image.
His Artificial intelligence study frequently intersects with other fields, such as Natural language processing.
Jiebo Luo has included themes like Benchmark and Closed captioning in his Natural language processing study.
His research integrates issues of Motion, Bayesian network, Feature and Visualization in his study of Pattern recognition.
In the subject of general Machine learning, his work in Recurrent neural network, Support vector machine and Feature vector is often linked to Semantic computing, thereby combining diverse domains of study.
His Image study combines topics from a wide range of disciplines, such as Metadata and Face detection.
His most cited work include:
- Learning multi-label scene classification (1442 citations)
- Recognizing realistic actions from videos “in the wild” (874 citations)
- Image Captioning with Semantic Attention (788 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Social media and Machine learning.
His work on Natural language processing expands to the thematically related Artificial intelligence.
His work in Digital image, Image processing, Image segmentation, Segmentation and Object detection is related to Computer vision.
The Pattern recognition study combines topics in areas such as Context and Feature.
His studies in Social media integrate themes in fields like Data science, Sentiment analysis, Scale and Internet privacy.
His study in Multimedia extends to World Wide Web with its themes.
He most often published in these fields:
- Artificial intelligence (64.10%)
- Computer vision (27.96%)
- Pattern recognition (21.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (64.10%)
- Pattern recognition (21.86%)
- Machine learning (13.46%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Machine learning, Image and Social media are his primary areas of study.
His studies deal with areas such as Computer vision and Natural language processing as well as Artificial intelligence.
Jiebo Luo has researched Pattern recognition in several fields, including Matching and Recurrent neural network.
His Machine learning research integrates issues from Contextual image classification and Context.
While the research belongs to areas of Image, Jiebo Luo spends his time largely on the problem of Theoretical computer science, intersecting his research to questions surrounding Representation.
His Social media research includes elements of China, Social psychology and Public opinion, Politics.
Between 2019 and 2021, his most popular works were:
- ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction (30 citations)
- Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language. (20 citations)
- TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Social media, Discriminative model and Image.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Natural language processing.
In the field of Pattern recognition, his study on Unsupervised learning overlaps with subjects such as Two-graph.
His biological study spans a wide range of topics, including Sentiment analysis and Topic model.
His Discriminative model research incorporates elements of Subspace topology, Semantics, Image fusion and Re identification.
Jiebo Luo interconnects Embedding and Translation in the investigation of issues within Image.
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