What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Image, Feature extraction and Kernel.
His research brings together the fields of Pattern recognition and Artificial intelligence.
His research on Computer vision frequently connects to adjacent areas such as Deconvolution.
His Image research is multidisciplinary, incorporating elements of Algorithm and Line segment.
As part of one scientific family, he deals mainly with the area of Feature extraction, narrowing it down to issues related to the Feature, and often Cognitive neuroscience of visual object recognition, Object detection, Object, Image segmentation and Feature.
The study incorporates disciplines such as Image resolution and Deblurring in addition to Kernel.
His most cited work include:
- Image deblurring with blurred/noisy image pairs (854 citations)
- Image completion with structure propagation (539 citations)
- Visual attribute transfer through deep image analogy (251 citations)
What are the main themes of his work throughout his whole career to date?
Lu Yuan mainly focuses on Artificial intelligence, Computer vision, Image, Pattern recognition and Feature.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His study in the fields of Image restoration, Motion blur, Deblurring and Image stabilization under the domain of Computer vision overlaps with other disciplines such as Portrait.
His Image research focuses on Parameterized complexity and how it connects with Smoothing, Noise reduction and Computation.
His work carried out in the field of Pattern recognition brings together such families of science as Texture and Robustness.
His Feature research incorporates elements of Stereoscopy and Source image.
He most often published in these fields:
- Artificial intelligence (75.70%)
- Computer vision (45.79%)
- Image (28.97%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (75.70%)
- Algorithm (16.82%)
- Image (28.97%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Algorithm, Image, Machine learning and Convolution.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition.
His Algorithm study incorporates themes from Adversarial system and Activation function.
His Image study is concerned with the field of Computer vision as a whole.
In general Machine learning, his work in Ranking is often linked to Architecture, Sample, Sampling and Network architecture linking many areas of study.
The concepts of his Convolution study are interwoven with issues in Perspective, Convolutional neural network and Dimensionality reduction.
Between 2019 and 2021, his most popular works were:
- Dynamic Convolution: Attention Over Convolution Kernels (46 citations)
- Rethinking Classification and Localization for Object Detection (29 citations)
- Cross-Domain Correspondence Learning for Exemplar-Based Image Translation (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Artificial neural network, Feature extraction, Image and Theoretical computer science.
His work deals with themes such as Machine learning and Computer vision, which intersect with Artificial intelligence.
Lu Yuan combines subjects such as Smoothing and Algorithm with his study of Artificial neural network.
The Algorithm study combines topics in areas such as Convolution, Activation function and Kernel.
His Feature extraction study combines topics in areas such as Semantics, Segmentation, Image segmentation and Image translation.
His studies in Theoretical computer science integrate themes in fields like Normalization, Image processing, Spatially adaptive, Parameterized complexity and Computation.
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