Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation has attracted much research interest in recent years considering its advantage of low labeling cost. Most of the advanced algorithms follow the design principle that expands and constrains the seed regions from class activation maps (CAM). As well-known, conventional CAM tends to be incomplete or over-activated due to weak supervision. Fortunately, we find that semantic segmentation has a characteristic of spatial transformation equivariance, which can form a few self-supervisions to help weakly supervised learning. This work mainly explores the advantages of scale equivariant constrains for CAM generation, formulated as a self-supervised scale equivariant network (SSENet). Specifically, a novel scale equivariant regularization is elaborately designed to ensure consistency of CAMs from the same input image with different resolutions. This novel scale equivariant regularization can guide the whole network to learn more accurate class activation. This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework. Extensive experiments on PASCAL VOC 2012 datasets demonstrate that our method achieves the state-of-the-art performance both quantitatively and qualitatively for weakly supervised semantic segmentation. Code has been made available.
Code (1)
Tasks
SegmentationSemantic SegmentationWeakly-supervised LearningWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as …
Data AugmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationWeakly supervised segmentation with cross-modality equivariant constraints
Weakly supervised learning has emerged as an appealing alternative to alleviate the need for large labeled datasets in semantic segmentation. Most current approaches exploit class activation maps (CAMs), which can be gen…
Data AugmentationSemantic SegmentationWeakly-supervised LearningWeakly supervised segmentationCapsule Network Projectors are Equivariant and Invariant Learners
Learning invariant representations has been the longstanding approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in representations, yet do so with highly p…
Self-Supervised LearningSelf-Supervised Learning for Group Equivariant Neural Networks
This paper proposes a method to construct pretext tasks for self-supervised learning on group equivariant neural networks. Group equivariant neural networks are the models whose structure is restricted to commute with th…
Self-Supervised LearningRotation-Equivariant Self-Supervised Method in Image Denoising
Self-supervised image denoising methods have garnered significant research attention in recent years, for this kind of method reduces the requirement of large training datasets. Compared to supervised methods, self-super…
DenoisingImage Denoising