# Robust Trust Region for Weakly Supervised Segmentation Source: [Robust Trust Region for Weakly Supervised Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:37:16.577Z Authors: Dmitrii Marin, Yuri Boykov Published: 2021-04-05T15:11:29.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Pascal VOC 21 classes (val)](): Semantic segmentation; Pascal VOC 21 classes (val); mIoU: 75.6. - [Semantic Segmentation on Cityscapes (val)](): Semantic segmentation; Cityscapes (val); mIoU: 69.5. - [Semantic Segmentation on ADE20K (val)](): Semantic segmentation; ADE20K (val); mIoU: 37.7. ## Original sources and implementation [Original paper]() [PDF]()