What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Jimei Yang mainly investigates Artificial intelligence, Pattern recognition, Image, Computer vision and Convolutional neural network.
His works in Pixel, Artificial neural network, Feature, Generative model and Inference are all subjects of inquiry into Artificial intelligence.
The various areas that Jimei Yang examines in his Pattern recognition study include Video tracking, Deep learning and Iterative reconstruction.
His study looks at the relationship between Image and topics such as Key, which overlap with Motion and Matching.
His biological study spans a wide range of topics, including Pascal and Color term.
The concepts of his Convolutional neural network study are interwoven with issues in Structure, Data mining and Task.
His most cited work include:
- Generative Image Inpainting with Contextual Attention (770 citations)
- Attribute2Image: Conditional Image Generation from Visual Attributes (520 citations)
- Free-Form Image Inpainting With Gated Convolution (393 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Image and Artificial neural network.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His research in the fields of Digital image, Rendering, View synthesis and Pose overlaps with other disciplines such as Process.
His work carried out in the field of Pattern recognition brings together such families of science as Visualization, Pascal, Generative model and Key.
In his work, Iterative reconstruction and Encoding is strongly intertwined with Feature, which is a subfield of Image.
His Pixel research includes themes of Structure, Convolutional neural network and Task.
He most often published in these fields:
- Artificial intelligence (89.78%)
- Computer vision (45.26%)
- Pattern recognition (36.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (89.78%)
- Computer vision (45.26%)
- Image (27.01%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Image, Digital image and Motion.
Many of his studies on Artificial intelligence apply to Document layout as well.
His Computer vision study combines topics in areas such as Artificial neural network, Human dynamics and Generative model.
Jimei Yang has researched Image in several fields, including Pixel and Feature.
His Digital image study combines topics in areas such as Ground truth, Computer graphics, Rendering and Generative adversarial network.
His Motion study deals with RGB color model intersecting with Monocular and Detector.
Between 2019 and 2021, his most popular works were:
- High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling (17 citations)
- AIM 2020 Challenge on Image Extreme Inpainting (11 citations)
- Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Image, Human dynamics and Inpainting.
As part of his studies on Artificial intelligence, Jimei Yang often connects relevant areas like Computer animation.
Jimei Yang studies Computer vision, focusing on Motion in particular.
He has included themes like RGB color model, Monocular and Detector in his Human dynamics study.
His work carried out in the field of Inpainting brings together such families of science as Feature, Pixel, Object, Upsampling and Generative model.
The study incorporates disciplines such as Deep learning, Discriminative model, Digital image and Generative grammar in addition to Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
