What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary scientific interests are in Artificial intelligence, Pattern recognition, Object detection, Machine learning and Segmentation.
In his work, he performs multidisciplinary research in Artificial intelligence and Block.
In his research, Classifier is intimately related to Pascal, which falls under the overarching field of Pattern recognition.
His Object detection study incorporates themes from Contextual image classification and Feature learning.
His research in the fields of Supervised learning overlaps with other disciplines such as Multi-task learning.
Xinggang Wang works mostly in the field of Segmentation, limiting it down to topics relating to Pixel and, in certain cases, Data mining and Convolution.
His most cited work include:
- TextBoxes: a fast text detector with a single deep neural network (384 citations)
- CCNet: Criss-Cross Attention for Semantic Segmentation (372 citations)
- DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection (348 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Object detection, Computer vision and Segmentation.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Machine learning research is multidisciplinary, incorporating perspectives in Representation and Eye tracking.
His study looks at the intersection of Pattern recognition and topics like Image with Face.
Within one scientific family, Xinggang Wang focuses on topics pertaining to Feature learning under Object detection, and may sometimes address concerns connected to Autoencoder.
The concepts of his Segmentation study are interwoven with issues in Pixel and Pose.
He most often published in these fields:
- Artificial intelligence (90.28%)
- Pattern recognition (54.86%)
- Object detection (30.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.28%)
- Pattern recognition (54.86%)
- Segmentation (24.31%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Segmentation, Object detection and Code.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.
His Pattern recognition study combines topics in areas such as Pixel, Pascal, Deep learning and Leverage.
The various areas that Xinggang Wang examines in his Segmentation study include Semi-supervised learning and Real image.
His studies in Object detection integrate themes in fields like Contextual image classification and Benchmark.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Cognitive neuroscience of visual object recognition, Similarity and Gesture.
Between 2019 and 2021, his most popular works were:
- Deep High-Resolution Representation Learning for Visual Recognition. (244 citations)
- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection (116 citations)
- A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT (114 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, Object detection, Pattern recognition, Code and Segmentation.
Xinggang Wang has researched Artificial intelligence in several fields, including Margin and Machine learning.
He usually deals with Object detection and limits it to topics linked to Contextual image classification and Representation.
His work on Feature learning and Discriminative model as part of general Pattern recognition study is frequently linked to Tomography, Computed tomography and Lesion, bridging the gap between disciplines.
Xinggang Wang has included themes like Pixel, Test set and Benchmark in his Discriminative model study.
His Pascal research includes elements of Classifier, Object detector, Cognitive neuroscience of visual object recognition and Convolutional neural network.
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