What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Xiao-Yuan Jing mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Computer vision.
The study incorporates disciplines such as Software and Rank in addition to Artificial intelligence.
His research ties Image and Pattern recognition together.
His Machine learning research includes elements of Test data, Data mining and Variable.
Xiao-Yuan Jing works mostly in the field of Discriminative model, limiting it down to topics relating to Discriminant and, in certain cases, Identification.
His Linear discriminant analysis research includes themes of Normalization and Signature recognition.
His most cited work include:
- A face and palmprint recognition approach based on discriminant DCT feature extraction (217 citations)
- Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning (133 citations)
- Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition (132 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Facial recognition system, Machine learning and Feature extraction.
His research investigates the connection between Artificial intelligence and topics such as Computer vision that intersect with problems in Convolution.
His work deals with themes such as Subspace topology and Image, which intersect with Pattern recognition.
His Facial recognition system study incorporates themes from Image processing, Pixel and Kernel.
His studies deal with areas such as Contextual image classification, Representation, Data mining and Metric as well as Machine learning.
His Feature extraction study incorporates themes from Manifold alignment, Nonlinear dimensionality reduction, Feature, Projection and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (98.25%)
- Pattern recognition (67.37%)
- Facial recognition system (26.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (98.25%)
- Pattern recognition (67.37%)
- Computer vision (24.21%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Feature are his primary areas of study.
His Artificial intelligence research includes themes of Machine learning and Metric.
His Pattern recognition research focuses on Feature vector in particular.
His Computer vision research integrates issues from Attention network and Convolution.
The Discriminative model study combines topics in areas such as Feature, Re identification, Feature learning, Benchmark and Convolutional neural network.
His study looks at the intersection of Facial recognition system and topics like Set with Identification and Empirical research.
Between 2019 and 2021, his most popular works were:
- Multiset Feature Learning for Highly Imbalanced Data Classification (13 citations)
- Modality-specific and shared generative adversarial network for cross-modal retrieval (12 citations)
- Dynamic attention network for semantic segmentation (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Machine learning, Discriminative model, Computer vision and Metric.
His study connects Pattern recognition and Artificial intelligence.
His study in Machine learning is interdisciplinary in nature, drawing from both Multiset, Discriminant and Partition.
His Discriminative model research is multidisciplinary, relying on both Multispectral image, Facial recognition system, Face, Benchmark and Convolutional neural network.
Xiao-Yuan Jing interconnects Attention network, Pyramid and Pascal in the investigation of issues within Computer vision.
His biological study spans a wide range of topics, including Software bug, Interpretability, Empirical research and Field.
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