What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Convolutional neural network, Machine learning and Computer vision.
His research in Robustness, Feature, Deep learning, Face and Segmentation are components of Artificial intelligence.
His study in the field of Normalization is also linked to topics like Brightness.
His Convolutional neural network study integrates concerns from other disciplines, such as Algorithm, Image, Parsing and Feature extraction.
Many of his research projects under Image are closely connected to Structure with Structure, tying the diverse disciplines of science together.
His work carried out in the field of Machine learning brings together such families of science as Inference, Face detection, Pose, Crowd density and Visualization.
His most cited work include:
- Image Super-Resolution Using Deep Convolutional Networks (3329 citations)
- Learning a Deep Convolutional Network for Image Super-Resolution (2436 citations)
- Accelerating the Super-Resolution Convolutional Neural Network (1051 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Convolutional neural network are his primary areas of study.
His Deep learning, Face, Segmentation, Feature and Image investigations are all subjects of Artificial intelligence research.
His study in the field of Superresolution also crosses realms of Measure.
His studies deal with areas such as Pascal and Robustness as well as Pattern recognition.
His Object, Image resolution and Compression artifact study in the realm of Computer vision interacts with subjects such as Field.
Chen Change Loy has researched Convolutional neural network in several fields, including Parsing, Artificial neural network, Inference, Algorithm and Feature extraction.
He most often published in these fields:
- Artificial intelligence (85.71%)
- Pattern recognition (31.93%)
- Machine learning (30.25%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (85.71%)
- Machine learning (30.25%)
- Computer vision (24.37%)
In recent papers he was focusing on the following fields of study:
Chen Change Loy spends much of his time researching Artificial intelligence, Machine learning, Computer vision, Segmentation and Code.
Artificial intelligence is closely attributed to Flow in his work.
His Machine learning research incorporates elements of Classifier, Facial recognition system and State.
The concepts of his Code study are interwoven with issues in Object detection, Software engineering and Task.
As a part of the same scientific study, Chen Change Loy usually deals with the Deep learning, concentrating on Gesture and frequently concerns with Face.
His Image warping research includes themes of Optical flow estimation and Image.
Between 2018 and 2021, his most popular works were:
- MMDetection: Open MMLab Detection Toolbox and Benchmark. (391 citations)
- Hybrid Task Cascade for Instance Segmentation (226 citations)
- EDVR: Video Restoration With Enhanced Deformable Convolutional Networks (194 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, Object detection, Feature, Code and Benchmark.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision.
He works mostly in the field of Object detection, limiting it down to concerns involving Segmentation and, occasionally, Leverage and Kernel.
The study incorporates disciplines such as Image resolution, Deblurring, Iterative reconstruction and Pyramid in addition to Code.
His biological study spans a wide range of topics, including Codebase, Modality and Software engineering.
Chen Change Loy interconnects Deep learning and Image segmentation in the investigation of issues within Feature learning.
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