Zhe Lin focuses on Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image. His Artificial intelligence research focuses on Artificial neural network, Deep learning, Facial recognition system, Classifier and Feature. His studies deal with areas such as Image resolution, Probabilistic logic, Contextual image classification and Face detection as well as Pattern recognition.
Zhe Lin interconnects Feature, Speech recognition, Singular value decomposition, Linear classifier and K-SVD in the investigation of issues within Contextual image classification. His work in Convolutional neural network covers topics such as Inference which are related to areas like Computation, Norm, Communication channel, Conditional random field and Single image. His work in the fields of Image, such as Inpainting, overlaps with other areas such as Process.
Zhe Lin spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image and Artificial neural network. His Artificial intelligence study frequently links to other fields, such as Machine learning. His Computer vision study typically links adjacent topics like Salient.
His biological study spans a wide range of topics, including Object detection and Embedding. His work on Inpainting, Similarity and Texture as part of his general Image study is frequently connected to Process and Set, thereby bridging the divide between different branches of science. Artificial neural network is frequently linked to Deep learning in his study.
His primary areas of investigation include Artificial intelligence, Computer vision, Image, Digital image and Object. His research integrates issues of Natural language processing and Pattern recognition in his study of Artificial intelligence. His study of Convolutional neural network is a part of Pattern recognition.
In most of his Computer vision studies, his work intersects topics such as Training set. In general Image, his work in Inpainting, Image based and Histogram is often linked to Set linking many areas of study. His work on Object detection as part of general Object research is often related to Scale, thus linking different fields of science.
Zhe Lin mainly focuses on Artificial intelligence, Computer vision, Segmentation, Image and Pattern recognition. His study connects Natural language processing and Artificial intelligence. His Computer vision study frequently draws connections between adjacent fields such as Convolutional neural network.
His work deals with themes such as Object and Pixel, which intersect with Image. His Pattern recognition study incorporates themes from Depth map, Spatial contextual awareness, State, Decoding methods and Ranking. His Artificial neural network study which covers Digital image that intersects with Deep learning.
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)
Generative Image Inpainting with Contextual Attention
Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
computer vision and pattern recognition (2018)
Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
Zhuolin Jiang;Zhe Lin;L. S. Davis.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Interactive facial feature localization
Vuong Le;Jonathan Brandt;Zhe Lin;Lubomir Bourdev.
european conference on computer vision (2012)
Coupled Dictionary Training for Image Super-Resolution
Jianchao Yang;Zhaowen Wang;Zhe Lin;S. Cohen.
IEEE Transactions on Image Processing (2012)
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
Zhuolin Jiang;Zhe Lin;Larry S. Davis.
computer vision and pattern recognition (2011)
Free-Form Image Inpainting With Gated Convolution
Jiahui Yu;Zhe Lin;Jimei Yang;Xiaohui Shen.
international conference on computer vision (2019)
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
Chao Yang;Xin Lu;Zhe Lin;Eli Shechtman.
computer vision and pattern recognition (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)
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