V
Vibhav Vineet
Researcher at Microsoft
Publications - 82
Citations - 10497
Vibhav Vineet is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 24, co-authored 59 publications receiving 8956 citations. Previous affiliations of Vibhav Vineet include International Institute of Information Technology, Hyderabad & Intel.
Papers
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Proceedings ArticleDOI
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng,Sadeep Jayasumana,Bernardino Romera-Paredes,Vibhav Vineet,Zhizhong Su,Dalong Du,Chang Huang,Philip H. S. Torr +7 more
TL;DR: In this article, a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced.
Proceedings ArticleDOI
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng,Sadeep Jayasumana,Bernardino Romera-Paredes,Vibhav Vineet,Zhizhong Su,Dalong Du,Chang Huang,Philip H. S. Torr +7 more
TL;DR: A new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced, and top results are obtained on the challenging Pascal VOC 2012 segmentation benchmark.
Journal ArticleDOI
Struck: Structured Output Tracking with Kernels
Sam Hare,Stuart Golodetz,Amir Saffari,Vibhav Vineet,Ming-Ming Cheng,Stephen Hicks,Philip H. S. Torr +6 more
TL;DR: A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance.
Book ChapterDOI
Playing for Data: Ground Truth from Computer Games
TL;DR: In this paper, the authors present an approach to rapidly create pixel-accurate semantic label maps for images extracted from modern computer games, which enables rapid propagation of semantic labels within and across images synthesized by the game, without access to the source code or the content.
Posted Content
Playing for Data: Ground Truth from Computer Games
TL;DR: It is shown that associations between image patches can be reconstructed from the communication between the game and the graphics hardware, which enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content.