Overview:

We are interested in algorithms that can find objects in images and video. Such algorithms have practical implications for applications such as image search and robotic navigation. Important and open issues include representation and learning - what makes a good model of an object, and how can one learn such a representation? To help answer such questions, we employ both generative and discriminative machine-learning techniques.

Publications:
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Multi-scale recognition with DAG-CNNs
Songfan Yang, Deva Ramanan
IEEE International Conference on Computer Vision, 2015.
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Where's Waldo: Matching People in Images of Crowds
Rahul Garg, Deva Ramanan, Steve Seitz, Noah Snavely
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
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Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
Deva Ramanan, Simon Baker
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2011.
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Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces
Carl Vondrick, Deva Ramanan, Donald Patterson
Proc. of the European Conference on Computer Vision, 2010.
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Object Detection with Discriminatively Trained Part-Based Models
Pedro Felzenszwalb, Ross Girshick, David McAllester, Deva Ramanan
IEEE Pattern Analysis and Machine Intelligence (PAMI), 2009.
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Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
Deva Ramanan, Simon Baker
International Conference on Computer Vision (ICCV), 2009.
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Increasing the density of active appearance models
Kannan Ramnath, Simon Baker, Ian Matthews, Deva Ramanan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
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A Discriminatively Trained, Multiscale, Deformable Part Model
Pedro Felzenszwalb, David McAllester, Deva Ramanan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
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Learning to parse images of articulated bodies
Deva Ramanan
Advances in Neural Information Processing Systems 19, 2007, 1129--1136.
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Building Models of Animals from Video
Deva Ramanan, D.A. Forsyth, Kobus Barnard
PAMI, 2006, 28, 8, 1319-1334.
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The Rate Adapting Poisson Model for Information Retrieval and Object Recognition
P. Gehler, A. Holub, Max Welling
Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 2006, 337-344.
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Cue Integration for Figure/Ground Labeling
Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
Advances in Neural Information Processing Systems 18, 2006, 1121--1128.
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Training Deformable Models for Localization
Deva Ramanan, Cristian Sminchisescu
CVPR, 2006, I: 206-213.
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Strike a Pose: Tracking People by Finding Stylized Poses
Deva Ramanan, D.A. Forsyth, Andrew Zisserman
CVPR, 2005, I: 271-278.
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Viewpoint-Invariant Learning and Detection of Human Heads
Markus Weber, Wolfgang Einhaeuser, Max Welling, Pietro Perona
AFGR, 2000, 20-27.
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Unsupervised Learning of Models for Recognition
Markus Weber, Max Welling, Pietro Perona
ECCV, 2000, I: 18-32.
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Towards Automatic Discovery of Object Categories
Markus Weber, Max Welling, Pietro Perona
CVPR, 2000, II: 101-108.
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