Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

Deepak Pathak, Philipp Krahenbuhl, Trevor Darrell; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1796-1804

Abstract


We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier. We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN. Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization. The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks. Extensive experiments demonstrate the generality of our new learning framework. The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation. We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.

Related Material


[pdf]
[bibtex]
@InProceedings{Pathak_2015_ICCV,
author = {Pathak, Deepak and Krahenbuhl, Philipp and Darrell, Trevor},
title = {Constrained Convolutional Neural Networks for Weakly Supervised Segmentation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}