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» Learning Generic Prior Models for Visual Computation
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EMMCVPR
2011
Springer
13 years 9 months ago
Multiple-Instance Learning with Structured Bag Models
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...
Jonathan Warrell, Philip H. S. Torr
ECCV
2006
Springer
15 years 11 months ago
Efficient Belief Propagation with Learned Higher-Order Markov Random Fields
Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on a...
Xiangyang Lan, Stefan Roth, Daniel P. Huttenlocher...
CVPR
2012
IEEE
13 years 5 days ago
Boosting bottom-up and top-down visual features for saliency estimation
Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. ...
Ali Borji
CVPR
2009
IEEE
16 years 5 months ago
Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework
Given an image, we propose a hierarchical generative model that classifies the overall scene, recognizes and segments each object component, as well as annotates the image with ...
Fei-Fei Li 0002, Li-Jia Li, Richard Socher
CVPR
1997
IEEE
15 years 2 months ago
Learning bilinear models for two-factor problems in vision
In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color...
William T. Freeman, Joshua B. Tenenbaum