Weakly supervised segmentation via instance-aware propagation

• Main Authors: XIN, Huang, ZHU, Qianshu, LIU, Yongtuo, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Weakly supervised learning; Instance segmentation; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7843; https://ink.library.smu.edu.sg/context/sis_research/article/8846/viewcontent/weakly.pdf
• Institution: Singapore Management University

Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our proposed proposal-matching strategy adds a re-balancing constraint on the contributions of multi-scale object proposals. Extensive experiments conducted on the PASCAL VOC 2012 dataset show the superior performance of our method over weakly-supervised state-of-the-arts for both semantic and instance segmentation.(c) 2021 Elsevier B.V. All rights reserved.