CVPR2012_oral

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 images instead, the method must predict a class label

for every pixel. Our goal is to enable segmentation algorithms

to use multiple visual cues in this weakly supervised

setting, analogous to what is achieved by fully supervised

methods. However, it is difficult to assess the relative usefulness

of different visual cues from weakly supervised training

data. We define a parametric family of structured models,

where each model weighs visual cues in a different way. We

propose a Maximum Expected Agreement model selection

principle that evaluates the quality of a model from the family

without looking at superpixel labels. Searching for the

best model is a hard optimization problem, which has no

analytic gradient and multiple local optima. We cast it as

a Bayesian optimization problem and propose an