Pseudo-Label-Free Weakly Supervised Semantic Segmentation Using Image Masking

Weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels. Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training. A potential drawback of the co...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.19401-19411
Main Authors: Kim, Sangtae, Nguyen, Luong Trung, Shim, Kyuhong, Kim, Junhan, Shim, Byonghyo
Format: Article
Language:English
Subjects:
Birds
image classification
Image segmentation
Location awareness
saliency map
Semantic segmentation
Semantics
Task analysis
Training
visual attention
Visualization
Weakly-supervised semantic segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels. Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training. A potential drawback of the conventional WSSS approaches is that the pseudo-label cannot accurately express the object regions and their classes, causing a degradation of the segmentation performance. In this paper, we propose a new WSSS technique that trains the segmentation network without relying on the pseudo-label. Key idea of the proposed approach is to train the segmentation network such that the object erased by the segmentation map is not detected by the classification network. From extensive experiments on the PASCAL VOC 2012 benchmark dataset, we demonstrate that our approach is effective in WSSS.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3149587