# Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation

arXiv: https://arxiv.org/abs/1909.03714 · HTML: https://mlsanity.com/paper/1909.03714/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Weakly supervised segmentation with cross-modality equivariant constraints](https://mlsanity.com/paper/2104.02488/) (2021)
- [Edge-competing Pathological Liver Vessel Segmentation with Limited Labels](https://mlsanity.com/paper/2108.00384/) (2021)
- [Boundary Knowledge Translation based Reference Semantic Segmentation](https://mlsanity.com/paper/2108.01075/) (2021)
- [Visual Boundary Knowledge Translation for Foreground Segmentation](https://mlsanity.com/paper/2108.00379/) (2021)
- [MECPformer: Multi-estimations Complementary Patch with CNN-Transformers for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.10689/) (2023)
- [Pseudo-label Refinement for Improving Self-Supervised Learning Systems](https://mlsanity.com/paper/2410.14242/) (2024)

## Abstract (arXiv)

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.