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CVPR
2012
IEEE
11 years 10 months ago
Learning to localize detected objects
In this paper, we propose an approach to accurately localize detected objects. The goal is to predict which features pertain to the object and define the object extent with segme...
Qieyun Dai, Derek Hoiem
NIPS
2001
13 years 9 months ago
Probabilistic principles in unsupervised learning of visual structure: human data and a model
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
NIPS
2003
13 years 9 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
UAI
2008
13 years 9 months ago
Model-Based Bayesian Reinforcement Learning in Large Structured Domains
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...
Stéphane Ross, Joelle Pineau
FTCGV
2011
122views more  FTCGV 2011»
12 years 11 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert