What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Yue Gao focuses on Artificial intelligence, Pattern recognition, Computer vision, Image retrieval and Information retrieval.
Many of his studies involve connections with topics such as Machine learning and Artificial intelligence.
His research ties Feature vector and Computer vision together.
His work on Semantic gap is typically connected to Explicit semantic analysis, Semantic grid and Semantic technology as part of general Image retrieval study, connecting several disciplines of science.
The various areas that Yue Gao examines in his Information retrieval study include Text mining, Data mining, Multimedia and Visual Word.
Yue Gao works mostly in the field of Contextual image classification, limiting it down to concerns involving Feature extraction and, occasionally, Convolutional neural network and Feature.
His most cited work include:
- 3-D Object Retrieval and Recognition With Hypergraph Analysis (453 citations)
- Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (360 citations)
- Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation (194 citations)
What are the main themes of his work throughout his whole career to date?
Yue Gao spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Information retrieval.
In his research, Yue Gao performs multidisciplinary study on Artificial intelligence and Hypergraph.
Yue Gao has included themes like Contextual image classification, Representation and Feature in his Pattern recognition study.
His work on Pixel, Image segmentation and Gaze as part of his general Computer vision study is frequently connected to Process and Spatial analysis, thereby bridging the divide between different branches of science.
Yue Gao combines subjects such as Classifier, Social media, Microblogging and Connectome with his study of Machine learning.
His Information retrieval research incorporates elements of Image retrieval, Visual Word and Data mining.
He most often published in these fields:
- Artificial intelligence (68.10%)
- Pattern recognition (36.20%)
- Computer vision (20.86%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (68.10%)
- Pattern recognition (36.20%)
- Hypergraph (15.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Hypergraph, Representation and Machine learning.
Deep learning, Feature extraction, Image, Discriminative model and Feature are the primary areas of interest in his Artificial intelligence study.
His work deals with themes such as Relation and Identification, which intersect with Pattern recognition.
His Representation research is multidisciplinary, incorporating perspectives in Artificial neural network, Point cloud and Cognitive neuroscience of visual object recognition.
His Machine learning research is multidisciplinary, incorporating elements of Training set, Pairwise comparison and Set.
The various areas that Yue Gao examines in his Convolutional neural network study include Text mining and Visualization.
Between 2017 and 2021, his most popular works were:
- GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition (154 citations)
- Hypergraph Neural Networks (114 citations)
- Predicting Personalized Image Emotion Perceptions in Social Networks (96 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Yue Gao mostly deals with Artificial intelligence, Machine learning, Representation, Feature extraction and Feature.
His work in Deep learning, Discriminative model, Convolutional neural network, Contextual image classification and Training set are all subfields of Artificial intelligence research.
His work in Machine learning addresses issues such as Pairwise comparison, which are connected to fields such as Object and Relevance.
The study incorporates disciplines such as Artificial neural network, Point cloud and Pattern recognition in addition to Representation.
In his research on the topic of Pattern recognition, Embedding and Cognitive neuroscience of visual object recognition is strongly related with Feature.
Yue Gao works mostly in the field of Feature extraction, limiting it down to topics relating to Visualization and, in certain cases, World Wide Web and Social network, as a part of the same area of interest.
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