Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/142802
Title: | Bootstrapping the performance of webly supervised semantic segmentation | Authors: | Shen, Tong Lin, Guosheng Shen, Chunhua Reid, Ian |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2018 | Source: | Shen, T., Lin, G., Shen, C., & Reid, I. (2018). Bootstrapping the performance of webly supervised semantic segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1363-1371. doi:10.1109/CVPR.2018.00148 | Abstract: | Fully supervised methods for semantic segmentation require pixel-level class masks to train, the creation of which is expensive in terms of manual labour and time. In this work, we focus on weak supervision, developing a method for training a high-quality pixel-level classifier for semantic segmentation, using only image-level class labels as the provided ground-truth. Our method is formulated as a two-stage approach in which we first aim to create accurate pixel-level masks for the training images via a bootstrapping process, and then use these now-accurately segmented images as a proxy ground-truth in a more standard supervised setting. The key driver for our work is that in the target dataset we typically have reliable ground-truth image-level labels, while data crawled from the web may have unreliable labels, but can be filtered to comprise only easy images to segment, therefore having reliable boundaries. These two forms of information are complementary and we use this observation to build a novel bi-directional transfer learning framework. This framework transfers knowledge between two domains, target domain and web domain, bootstrapping the performance of weakly supervised semantic segmentation. Conducting experiments on the popular benchmark dataset PASCAL VOC 2012 based on both a VGG16 network and on ResNet50, we reach state-of-the-art performance with scores of 60.2% IoU and 63.9% IoU respectively. | URI: | https://hdl.handle.net/10356/142802 | ISBN: | 978-1-5386-6421-6 | DOI: | 10.1109/CVPR.2018.00148 | Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CVPR.2018.00148. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Bootstrapping the Performance of Webly Supervised Semantic Segmentation.pdf | 3.86 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
10
49
Updated on Jan 28, 2023
Web of ScienceTM
Citations
10
23
Updated on Jan 25, 2023
Page view(s)
222
Updated on Jan 31, 2023
Download(s) 50
50
Updated on Jan 31, 2023
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.