Addressing Noisy Pixels in Weakly Supervised Semantic Segmentation with Weights Assigned

Author:

Qian Feng1ORCID,Yang Juan2,Tang Sipeng3,Chen Gao4ORCID,Yan Jingwen2

Affiliation:

1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

2. College of Engineering, Shantou University, Shantou 515063, China

3. China Mobile Communications Group Guangdong Co., Ltd. Shantou Branch, Shantou 515041, China

4. School of Telecommunications Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China

Abstract

Weakly supervised semantic segmentation (WSSS) aims to segment objects without a heavy burden of dense annotations. Pseudo-masks serve as supervisory information for training segmentation models, which is crucial to the performance of segmentation models. However, the generated pseudo-masks contain significant noisy labels, which leads to poor performance of the segmentation models trained on these pseudo-masks. Few studies address this issue, as these noisy labels remain inevitable even after the pseudo-masks are improved. In this paper, we propose an uncertainty-weight transform module to mitigate the impact of noisy labels on model performance. It is noteworthy that our approach is not aimed at eliminating noisy labels but rather enhancing the robustness of the model to noisy labels. The proposed method adopts a frequency-based approach to estimate pixel uncertainty. Moreover, the uncertainty of pixels is transformed into loss weights through a set of well-designed functions. After dynamically assigning weights, the model allocates attention to each pixel in a significantly differentiated manner. Meanwhile, the impact of noisy labels on model performance is weakened. Experiments validate the effectiveness of the proposed method, achieving state-of-the-art results of 69.3% on PASCAL VOC 2012 and 39.3% on MS COCO 2014, respectively.

Funder

State key laboratory major special projects of Jilin Province Science and Technology Development Plan

Guangdong Provincial University Innovation Team Project

Guangdong Province Natural Science Foundation

Songshan Lake Sci-tech Commissoner Program

Publisher

MDPI AG

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