What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Object detection and Machine learning.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer vision.
His work carried out in the field of Pattern recognition brings together such families of science as Contrast and Salience.
His Feature extraction study combines topics in areas such as Hyperspectral imaging, Pixel, Feature learning, Visualization and Image retrieval.
His studies in Object detection integrate themes in fields like Object-class detection, Image, Viola–Jones object detection framework and Field.
His Machine learning study integrates concerns from other disciplines, such as Sparse approximation, Robustness and Benchmark.
His most cited work include:
- Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (762 citations)
- Remote Sensing Image Scene Classification: Benchmark and State of the Art (612 citations)
- A survey on object detection in optical remote sensing images (539 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction are his primary areas of study.
His Artificial intelligence study is mostly concerned with Discriminative model, Object detection, Deep learning, Convolutional neural network and Feature.
The concepts of his Object detection study are interwoven with issues in Artificial neural network, Supervised learning and Remote sensing.
His research integrates issues of Resting state fMRI and Salience in his study of Pattern recognition.
His research investigates the connection between Machine learning and topics such as Sparse approximation that intersect with problems in Functional magnetic resonance imaging.
In his work, Canonical correlation is strongly intertwined with Feature selection, which is a subfield of Feature extraction.
He most often published in these fields:
- Artificial intelligence (72.57%)
- Pattern recognition (41.43%)
- Computer vision (22.29%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.57%)
- Pattern recognition (41.43%)
- Machine learning (21.43%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Object detection and Feature extraction.
His studies in Convolutional neural network, Benchmark, Object, Discriminative model and Feature are all subfields of Artificial intelligence research.
His Pattern recognition research integrates issues from RGB color model, Modality, Correlation and Salient object detection.
His Artificial neural network study, which is part of a larger body of work in Machine learning, is frequently linked to Quantitative trait locus, bridging the gap between disciplines.
Junwei Han interconnects Supervised learning, Pascal, Remote sensing and Salience in the investigation of issues within Object detection.
His studies deal with areas such as Cognitive neuroscience of visual object recognition and Binary descriptor as well as Feature extraction.
Between 2019 and 2021, his most popular works were:
- Object detection in optical remote sensing images: A survey and a new benchmark (102 citations)
- Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (37 citations)
- P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization. (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Object detection, Feature extraction, Pattern recognition and Object.
The study incorporates disciplines such as Machine learning and Task analysis in addition to Artificial intelligence.
His research investigates the connection between Object detection and topics such as Discriminative model that intersect with issues in Visualization, Categorization and Detector.
His Feature extraction research is multidisciplinary, relying on both Supervised learning and Segmentation.
His biological study spans a wide range of topics, including RGB color model, Modality, Upper and lower bounds and Salience.
Junwei Han works mostly in the field of Object, limiting it down to topics relating to Remote sensing and, in certain cases, Focus and Learning object, as a part of the same area of interest.
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