Complementary Patch for Weakly Supervised Semantic Segmentation

Fei Zhang, Chaochen Gu, Chenyue Zhang, Yuchao Dai

科研成果: 书/报告/会议事项章节会议稿件同行评审

117 引用 (Scopus)

摘要

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to generate the pseudo labels for semantic segmentation. However, CAM merely discovers seeds from a small number of regions, which may be insufficient to serve as pseudo masks for semantic segmentation. In this paper, we formulate the expansion of object regions in CAM as an increase in information. From the perspective of information theory, we propose a novel Complementary Patch (CP) Representation and prove that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts, namely CP Pair, is greater than or equal to the information of the baseline CAM. Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by a triplet network and three regularization functions. To further improve the quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to augment the contextual information by using object-region relations between the feature maps and the CAMs. Experimental results on the PASCAL VOC 2012 datasets show that our proposed method achieves a new state-of-the-art in WSSS, validating the effectiveness of our CP Representation and CPN.

源语言英语
主期刊名Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
出版商Institute of Electrical and Electronics Engineers Inc.
7222-7231
页数10
ISBN(电子版)9781665428125
DOI
出版状态已出版 - 2021
活动18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, 加拿大
期限: 11 10月 202117 10月 2021

出版系列

姓名Proceedings of the IEEE International Conference on Computer Vision
ISSN(印刷版)1550-5499

会议

会议18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
国家/地区加拿大
Virtual, Online
时期11/10/2117/10/21

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