Image-Level Supervised Instance Segmentation Using Instance-Wise Boundary

Recently, most image-level supervised instance segmentation methods extend Class Attention Maps (CAMs) to find the entire instance masks. Inter-pixel Relation Network (IRNet) can effectively generate the class-wise boundary maps for attention score propagation. However, class-wise boundary is likely...

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Bibliographic Details
Main Authors: Yang, Yuyuan, Hou, Ya-Li, Hou, Zhijiang, Hao, Xiaoli, Shen, Yan
Format: Conference Proceeding
Language:English
Subjects:
Annotations
Cams
Conferences
Data mining
Image segmentation
Image-level supervision
Instance segmentation
Weakly supervised
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Summary:Recently, most image-level supervised instance segmentation methods extend Class Attention Maps (CAMs) to find the entire instance masks. Inter-pixel Relation Network (IRNet) can effectively generate the class-wise boundary maps for attention score propagation. However, class-wise boundary is likely to cause the failure of segmentation among instances. In this work, we find instance-wise information can be extracted from the displacement field of IRNet. Motivated by the observations, an improved IRNet-based instance segmentation method with instance-wise boundary has been developed. Experimental results based on PASCAL VOC 2012 demonstrate the effectiveness of our proposed method. Compared with the recent state-of-the-art methods, the mean average precision can be increased by 4.3% without any additional annotations.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506011