Robust Trust Region for Weakly Supervised Segmentation
This addresses the high cost of pixel-level annotation in segmentation for computer vision applications, offering an incremental improvement over existing weakly supervised methods.
The paper tackles the problem of training semantic segmentation models with limited pixel-level labels by introducing a robust trust region approach that integrates strong low-level solvers for regularization, achieving state-of-the-art results.
Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly supervised settings, e.g. where a fraction of pixels is labeled or when only image-level tags are available. It has been shown that regularized losses - originally developed for unsupervised low-level segmentation and representing geometric priors on pixel labels - can considerably improve the quality of weakly supervised training. However, many common priors require optimization stronger than gradient descent. Thus, such regularizers have limited applicability in deep learning. We propose a new robust trust region approach for regularized losses improving the state-of-the-art results. Our approach can be seen as a higher-order generalization of the classic chain rule. It allows neural network optimization to use strong low-level solvers for the corresponding regularizers, including discrete ones.