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, Pattern recognition, Convolutional neural network, Computer vision and Image.
His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Categorization.
His biological study spans a wide range of topics, including Object, Boosting, Inference and Image retrieval.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Segmentation and Image segmentation.
His Inpainting study in the realm of Image interacts with subjects such as Process.
His Inpainting research is multidisciplinary, incorporating elements of Generative model and Feature.
His most cited work include:
- A convolutional neural network cascade for face detection (863 citations)
- Generative Image Inpainting with Contextual Attention (770 citations)
- A unified approach to salient object detection via low rank matrix recovery (596 citations)
What are the main themes of his work throughout his whole career to date?
Xiaohui Shen spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image and Convolutional neural network.
His research on Artificial intelligence often connects related areas such as Machine learning.
The study incorporates disciplines such as Salient and Leverage in addition to Computer vision.
His Pattern recognition research includes elements of Object detection and Feature.
His Image research integrates issues from Word, Deep learning and Ranking.
His biological study deals with issues like Relevance, which deal with fields such as Embedding.
He most often published in these fields:
- Artificial intelligence (89.67%)
- Computer vision (43.66%)
- Pattern recognition (42.72%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (89.67%)
- Computer vision (43.66%)
- Image (31.92%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Image, Digital image and Artificial neural network.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
The Training set research Xiaohui Shen does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Decoupling, therefore creating a link between diverse domains of science.
His Image research incorporates elements of Ranking and Convolutional neural network.
His Digital image research includes themes of Depth plane, Joint and Depth of field.
His research integrates issues of Ranking and Discriminative model in his study of Deep learning.
Between 2019 and 2021, his most popular works were:
- Learning progressive joint propagation for human motion prediction (9 citations)
- EnlightenGAN: Deep Light Enhancement Without Paired Supervision (7 citations)
- Digital image completion using deep learning (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Computer vision, Human motion, Pixel and Machine learning.
He works on Artificial intelligence which deals in particular with Deep learning.
His Artificial neural network research extends to Computer vision, which is thematically connected.
His Artificial neural network research is multidisciplinary, relying on both Generative grammar and Discriminative model.
His Pixel study frequently draws connections to other fields, such as Matching.
The various areas that Xiaohui Shen examines in his Machine learning study include Visualization, Image restoration, Flexibility and Benchmark.
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