What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Optical flow, Pixel and Image restoration.
He studies Computer vision, focusing on Feature in particular.
His work on Optical flow estimation is typically connected to Adaptive optics as part of general Optical flow study, connecting several disciplines of science.
The study incorporates disciplines such as Mathematical optimization, Image warping, Robustness and Median filter in addition to Optical flow estimation.
His Pixel research incorporates elements of Optimization problem, Training set, Deblurring and Unsupervised learning.
His biological study spans a wide range of topics, including Image resolution, Kernel and Superresolution.
His most cited work include:
- Secrets of optical flow estimation and their principles (1049 citations)
- PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume (826 citations)
- NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (555 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Optical flow, Pixel and Pattern recognition.
Deqing Sun combines subjects such as Key and Benchmark with his study of Computer vision.
His Optical flow estimation study in the realm of Optical flow interacts with subjects such as Adaptive optics.
His research integrates issues of Image warping and Robustness in his study of Optical flow estimation.
His research in Pixel focuses on subjects like Convolutional neural network, which are connected to DUAL.
Deqing Sun interconnects Image resolution and Image restoration in the investigation of issues within Kernel.
He most often published in these fields:
- Artificial intelligence (90.80%)
- Computer vision (64.37%)
- Optical flow (40.23%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (90.80%)
- Computer vision (64.37%)
- Optical flow (40.23%)
In recent papers he was focusing on the following fields of study:
Deqing Sun mainly focuses on Artificial intelligence, Computer vision, Optical flow, Feature vector and Pixel.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His specific area of interest is Optical flow, where he studies Optical flow estimation.
His Optical flow estimation research includes elements of Empirical research and Image warping.
Deqing Sun has included themes like Algorithm, Feature and Robustness in his Feature vector study.
He has included themes like Encoder and Neural network system in his Pixel study.
Between 2019 and 2021, his most popular works were:
- Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation (53 citations)
- Learnable cost volume using the cayley representation (3 citations)
- Hierarchical Contrastive Motion Learning for Video Action Recognition (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Deqing Sun focuses on Optical flow, Optical flow estimation, Robustness, Positive-definite kernel and Positive definiteness.
His Optical flow study is concerned with Artificial intelligence in general.
His Optical flow estimation research spans across into fields like Volume, Adaptive optics and Network architecture.
His research in Robustness intersects with topics in Algorithm and Feature vector.
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