On Regularized Losses for Weakly-supervised CNN Segmentation

Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 507-522


Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segmentation currently dominated by methods mimicking full supervision via ``fake'' fully-labeled masks (proposals) generated from available partial input. To obtain such full masks the typical methods explicitly use standard regularization techniques for ``shallow'' segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such standard regularizers directly into the loss functions over partial input. This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training. This paper proposes and experimentally compares different losses integrating MRF/CRF regularization terms. We juxtapose our regularized losses with earlier proposal-generation methods. Our approach achieves state-of-the-art accuracy in semantic segmentation with near full-supervision quality.

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[pdf] [arXiv]
author = {Tang, Meng and Perazzi, Federico and Djelouah, Abdelaziz and Ben Ayed, Ismail and Schroers, Christopher and Boykov, Yuri},
title = {On Regularized Losses for Weakly-supervised CNN Segmentation},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}