What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Image.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His studies deal with areas such as Facial recognition system and Feature as well as Pattern recognition.
Wangmeng Zuo interconnects Mixture model and Affine transformation in the investigation of issues within Computer vision.
He combines subjects such as Time complexity, Visualization and Residual with his study of Discriminative model.
His Image study incorporates themes from Deep learning and Face.
His most cited work include:
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2693 citations)
- Weighted Nuclear Norm Minimization with Application to Image Denoising (990 citations)
- Learning Deep CNN Denoiser Prior for Image Restoration (807 citations)
What are the main themes of his work throughout his whole career to date?
Wangmeng Zuo spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image.
Artificial intelligence is frequently linked to Machine learning in his study.
His Pattern recognition research incorporates themes from Facial recognition system, Noise reduction, Image restoration and Metric.
The various areas that he examines in his Computer vision study include Visualization and Robustness.
His biological study spans a wide range of topics, including Artificial neural network, Object detection and Noise.
His studies deal with areas such as Pixel and Face as well as Image.
He most often published in these fields:
- Artificial intelligence (82.54%)
- Pattern recognition (49.69%)
- Computer vision (25.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (82.54%)
- Pattern recognition (49.69%)
- Image (17.05%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Image, Convolutional neural network and Computer vision.
He incorporates Artificial intelligence and Block in his studies.
His work in the fields of Pattern recognition, such as Feature extraction, intersects with other areas such as Component.
The Image study combines topics in areas such as Convolution and Benchmark.
His Convolutional neural network study incorporates themes from Normalization, Pooling, Computer engineering, Object detection and Residual.
In general Computer vision study, his work on Image warping, Segmentation, Structural similarity and Landmark often relates to the realm of Pipeline, thereby connecting several areas of interest.
Between 2019 and 2021, his most popular works were:
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (123 citations)
- Image denoising using deep CNN with batch renormalization. (77 citations)
- Attention-guided CNN for image denoising. (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Wangmeng Zuo mainly focuses on Artificial intelligence, Pattern recognition, Image, Convolutional neural network and Noise reduction.
His Artificial intelligence research is multidisciplinary, relying on both Metric, Residual and Computer vision.
In general Computer vision, his work in Structural similarity, Image segmentation, Image signal and Normalization is often linked to Pipeline linking many areas of study.
His Pattern recognition research incorporates themes from Convolution and Feature.
His work in Image covers topics such as Benchmark which are related to areas like Divide and conquer algorithms and Sharpening.
His research in Noise reduction intersects with topics in Noise and Noise.
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