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» Accurate max-margin training for structured output spaces
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CVPR
2012
IEEE
11 years 7 months ago
Weakly supervised structured output learning for semantic segmentation
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
IEEEICCI
2009
IEEE
13 years 3 months ago
Learning from an ensemble of Receptive Fields
Abstract-In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as Shape Encodi...
Hanlin Goh, Joo Hwe Lim, Chai Quek
TNN
2011
104views more  TNN 2011»
13 years 5 days ago
Extended Input Space Support Vector Machine
—In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from ...
Ricardo Santiago-Mozos, Fernando Pérez-Cruz...
CVPR
2012
IEEE
11 years 7 months ago
From Pictorial Structures to deformable structures
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connecte...
Silvia Zuffi, Oren Freifeld, Michael J. Black
WSC
2007
13 years 7 months ago
Visualization techniques utilizing the sensitivity analysis of models
Models of real world systems are being increasingly generated from data that describes the behaviour of systems. Data mining techniques, such as Artificial Neural Networks (ANN),...
Ivo Kondapaneni, Pavel Kordík, Pavel Slav&i...