J
Jenny Yuen
Researcher at Massachusetts Institute of Technology
Publications - 19
Citations - 4806
Jenny Yuen is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Object (computer science) & Motion field. The author has an hindex of 12, co-authored 19 publications receiving 4267 citations. Previous affiliations of Jenny Yuen include University of Washington.
Papers
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Journal ArticleDOI
SIFT Flow: Dense Correspondence across Scenes and Its Applications
TL;DR: SIFT flow is proposed, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence.
Book ChapterDOI
SIFT Flow: Dense Correspondence across Different Scenes
TL;DR: A method to align an image to its neighbors in a large image collection consisting of a variety of scenes, and applies the SIFT flow algorithm to two applications: motion field prediction from a single static image and motion synthesis via transfer of moving objects.
Proceedings ArticleDOI
A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xioyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A new large-scale video dataset designed to assess the performance of diverseVisual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage is introduced.
Journal ArticleDOI
Nonparametric Scene Parsing via Label Transfer
TL;DR: This paper proposes a novel, nonparametric approach for object recognition and scene parsing using a new technology the authors name label transfer, which is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
Proceedings ArticleDOI
Nonparametric scene parsing: Label transfer via dense scene alignment
TL;DR: Compared to existing object recognition approaches that require training for each object category, the proposed nonparametric scene parsing system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.