What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Pattern recognition, Facial recognition system, Computer vision and Manifold.
In most of his Artificial intelligence studies, his work intersects topics such as Linear subspace.
His Pattern recognition study integrates concerns from other disciplines, such as Machine learning and Covariance matrix.
His Facial recognition system research includes elements of Embedding and Biometrics.
Ruiping Wang has included themes like Representation, Expression, Discriminative model and Convolutional neural network in his Computer vision study.
His Convolutional neural network research is multidisciplinary, incorporating elements of Binary image, Feature detection and Visual Word, Image retrieval, Automatic image annotation.
His most cited work include:
- Deep Supervised Hashing for Fast Image Retrieval (499 citations)
- Manifold-Manifold Distance with application to face recognition based on image set (370 citations)
- Covariance discriminative learning: A natural and efficient approach to image set classification (359 citations)
What are the main themes of his work throughout his whole career to date?
Ruiping Wang mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Discriminative model.
His work carried out in the field of Artificial intelligence brings together such families of science as Manifold and Machine learning.
His work on Linear discriminant analysis as part of general Pattern recognition study is frequently linked to Set, therefore connecting diverse disciplines of science.
The study incorporates disciplines such as Supervised learning and Isomap in addition to Computer vision.
His work investigates the relationship between Facial recognition system and topics such as Manifold alignment that intersect with problems in Contextual image classification.
His Discriminative model study combines topics in areas such as Representation, Transformation, Expression and k-nearest neighbors algorithm.
He most often published in these fields:
- Artificial intelligence (90.72%)
- Pattern recognition (50.52%)
- Computer vision (26.80%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (90.72%)
- Binary code (12.37%)
- Pattern recognition (50.52%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Binary code, Pattern recognition, Visualization and Natural language processing.
Ruiping Wang has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
In general Machine learning study, his work on Overfitting and Discriminative model often relates to the realm of Knowledge transfer, thereby connecting several areas of interest.
His Visualization research incorporates elements of Semantics and Embedding.
His work on Question answering and Parsing as part of general Natural language processing study is frequently linked to Hierarchy, bridging the gap between disciplines.
When carried out as part of a general Face research project, his work on Facial recognition system is frequently linked to work in Code, therefore connecting diverse disciplines of study.
Between 2018 and 2021, his most popular works were:
- Transferable Contrastive Network for Generalized Zero-Shot Learning (45 citations)
- Exploring Context and Visual Pattern of Relationship for Scene Graph Generation (27 citations)
- Cross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Visualization, Task analysis, Machine learning and Feature extraction.
Artificial intelligence and Pattern recognition are commonly linked in his work.
His Visualization research incorporates themes from Semantics, Embedding and Robustness.
In the field of Machine learning, his study on Feature overlaps with subjects such as Association.
In Feature extraction, Ruiping Wang works on issues like Scene graph, which are connected to Discriminative model, Visual reasoning, Cognitive neuroscience of visual object recognition, Deep learning and Intersection.
The various areas that Ruiping Wang examines in his Image retrieval study include Matching and Information retrieval.
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