What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Xiaochun Cao focuses on Artificial intelligence, Pattern recognition, Computer vision, Image and Cluster analysis.
His biological study spans a wide range of topics, including Machine learning and Augmented Lagrangian method.
As a member of one scientific family, Xiaochun Cao mostly works in the field of Pattern recognition, focusing on Artificial neural network and, on occasion, Channel and Scale.
In the subject of general Computer vision, his work in Kernel, Deblurring and Feature is often linked to Kernel density estimation, thereby combining diverse domains of study.
His work deals with themes such as Range, Encoder, Benchmark and Contrast, which intersect with Image.
In his study, which falls under the umbrella issue of Cluster analysis, Redundancy and Image quality is strongly linked to Data mining.
His most cited work include:
- Single Image Dehazing via Multi-scale Convolutional Neural Networks (592 citations)
- Cluster-Based Co-Saliency Detection (295 citations)
- Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (293 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Image.
His study in Feature extraction, Feature, Robustness, Convolutional neural network and Segmentation are all subfields of Artificial intelligence.
The Pattern recognition study combines topics in areas such as Artificial neural network, Visualization, Cluster analysis and Benchmark.
His Cluster analysis study combines topics from a wide range of disciplines, such as Data mining and Outlier.
His study in the field of Ranking and Ranking also crosses realms of Crowdsourcing, Preference learning and Task.
Many of his studies on Image involve topics that are commonly interrelated, such as Salient.
He most often published in these fields:
- Artificial intelligence (82.46%)
- Computer vision (42.11%)
- Pattern recognition (32.98%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.46%)
- Pattern recognition (32.98%)
- Computer vision (42.11%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Feature and Machine learning.
His works in Feature extraction, Visualization, Convolutional neural network, Adversarial system and Video tracking are all subjects of inquiry into Artificial intelligence.
Xiaochun Cao interconnects Artificial neural network, Facial expression, Prior probability and Rendering in the investigation of issues within Convolutional neural network.
Xiaochun Cao interconnects Representation, Process and Similarity in the investigation of issues within Pattern recognition.
His study in the fields of Image segmentation and View synthesis under the domain of Computer vision overlaps with other disciplines such as Transmission and Field.
His Feature research is multidisciplinary, relying on both Minimum bounding box, Feature vector and Benchmark.
Between 2019 and 2021, his most popular works were:
- Generalized Latent Multi-View Subspace Clustering (137 citations)
- Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (43 citations)
- Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Deep learning are his primary areas of study.
In his work, he performs multidisciplinary research in Artificial intelligence and Noise measurement.
His work in the fields of Computer vision, such as Image segmentation, overlaps with other areas such as Transmission.
His work deals with themes such as Attention network, Process, Single image and Task, which intersect with Pattern recognition.
Xiaochun Cao has researched Feature extraction in several fields, including Visualization and Feature, Pyramid.
His research integrates issues of Adversarial system, Data mining, Leverage and Image retrieval in his study of Deep learning.
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