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.
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.
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.
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.
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.
A convolutional neural network cascade for face detection
Haoxiang Li;Zhe Lin;Xiaohui Shen;Jonathan Brandt.
computer vision and pattern recognition (2015)
A unified approach to salient object detection via low rank matrix recovery
Xiaohui Shen;Ying Wu.
computer vision and pattern recognition (2012)
Generative Image Inpainting with Contextual Attention
Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
computer vision and pattern recognition (2018)
Free-Form Image Inpainting With Gated Convolution
Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
international conference on computer vision (2019)
Towards unified depth and semantic prediction from a single image
Peng Wang;Xiaohui Shen;Zhe Lin;Scott Cohen.
computer vision and pattern recognition (2015)
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation
Yunchao Wei;Xiaodan Liang;Yunpeng Chen;Xiaohui Shen.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Top-Down Neural Attention by Excitation Backprop
Jianming Zhang;Sarah Adel Bargal;Zhe Lin;Jonathan Brandt.
International Journal of Computer Vision (2018)
Top-Down Neural Attention by Excitation Backprop
Jianming Zhang;Zhe L. Lin;Jonathan Brandt;Xiaohui Shen.
european conference on computer vision (2016)
Minimum Barrier Salient Object Detection at 80 FPS
Jianming Zhang;Stan Sclaroff;Zhe Lin;Xiaohui Shen.
international conference on computer vision (2015)
Semantic Object Parsing with Graph LSTM
Xiaodan Liang;Xiaohui Shen;Jiashi Feng;Liang Lin.
european conference on computer vision (2016)
Adobe Systems (United States)
Adobe Systems (United States)
Sun Yat-sen University
National University of Singapore
Adobe Systems (United States)
Sun Yat-sen University
Adobe Systems (United States)
Adobe Systems (United States)
Adobe Systems (United States)
National University of Singapore
Profile was last updated on December 6th, 2021.
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The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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