D
Deva Ramanan
Researcher at Carnegie Mellon University
Publications - 246
Citations - 70057
Deva Ramanan is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 74, co-authored 213 publications receiving 52607 citations. Previous affiliations of Deva Ramanan include University of California, Berkeley & Toyota Technological Institute at Chicago.
Papers
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Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Journal ArticleDOI
Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Proceedings ArticleDOI
A discriminatively trained, multiscale, deformable part model
TL;DR: A discriminatively trained, multiscale, deformable part model for object detection, which achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge and outperforms the best results in the 2007 challenge in ten out of twenty categories.
Proceedings ArticleDOI
Face detection, pose estimation, and landmark localization in the wild
Xiangxin Zhu,Deva Ramanan +1 more
TL;DR: It is shown that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures, in real-world, cluttered images.
Proceedings ArticleDOI
Articulated pose estimation with flexible mixtures-of-parts
Yi Yang,Deva Ramanan +1 more
TL;DR: A general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations, and it is shown that such relations can capture notions of local rigidity.