# Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace

arXiv: https://arxiv.org/abs/2102.09896 · HTML: https://mlsanity.com/paper/2102.09896/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference](https://mlsanity.com/paper/1902.10421/) (2019)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)
- [On Regularized Losses for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1803.09569/) (2018)
- [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018)
- [Learning random-walk label propagation for weakly-supervised semantic segmentation](https://mlsanity.com/paper/1802.00470/) (2018)
- [Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1904.11693/) (2019)
- [CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1811.10842/) (2018)
- [Min-Entropy Latent Model for Weakly Supervised Object Detection](https://mlsanity.com/paper/1902.06057/) (2019)
- [Backpropagation-Friendly Eigendecomposition](https://mlsanity.com/paper/1906.09023/) (2019)
- [Convolutional Random Walk Networks for Semantic Image Segmentation](https://mlsanity.com/paper/1605.07681/) (2016)
- [Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses](https://mlsanity.com/paper/1803.08071/) (2018)
- [DifNet: Semantic Segmentation by Diffusion Networks](https://mlsanity.com/paper/1805.08015/) (2018)
- [Super Diffusion for Salient Object Detection](https://mlsanity.com/paper/1811.09038/) (2018)

## Built on by (most cited first, 19 of 19)

- [Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02909/) (2022)
- [Diffusion Models and Representation Learning: A Survey](https://mlsanity.com/paper/2407.00783/) (2024)
- [Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation](https://mlsanity.com/paper/2203.10739/) (2022)
- [Weakly-Supervised Camouflaged Object Detection with Scribble Annotations](https://mlsanity.com/paper/2207.14083/) (2022)
- [SATS: Self-Attention Transfer for Continual Semantic Segmentation](https://mlsanity.com/paper/2203.07667/) (2022)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [Dynamic Feature Regularized Loss for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.01296/) (2021)
- [SASFormer: Transformers for Sparsely Annotated Semantic Segmentation](https://mlsanity.com/paper/2212.02019/) (2022)
- [Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label](https://mlsanity.com/paper/2402.17555/) (2024)
- [Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets](https://mlsanity.com/paper/2408.12489/) (2024)
- [Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation](https://mlsanity.com/paper/2507.01721/) (2025)
- [Synthesize Boundaries: A Boundary-aware Self-consistent Framework for Weakly Supervised Salient Object Detection](https://mlsanity.com/paper/2212.01764/) (2022)
- [Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.04949/) (2023)
- [Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels](https://mlsanity.com/paper/2401.12535/) (2024)
- [MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2409.19370/) (2024)
- [SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours](https://mlsanity.com/paper/2505.18989/) (2025)
- [Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2404.00918/) (2024)
- [Do Instance Priors Help Weakly Supervised Semantic Segmentation?](https://mlsanity.com/paper/2604.11170/) (2026)
- [TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation](https://mlsanity.com/paper/2607.07169/) (2026)

## Abstract (arXiv)

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Due to the lack of supervision, confident and consistent predictions are usually hard to obtain. Typically, people handle these problems to either adopt an auxiliary task with the well-labeled dataset or incorporate the graphical model with additional requirements on scribble annotations. Instead, this work aims to achieve semantic segmentation by scribble annotations directly without extra information and other limitations. Specifically, we propose holistic operations, including minimizing entropy and a network embedded random walk on neural representation to reduce uncertainty. Given the probabilistic transition matrix of a random walk, we further train the network with self-supervision on its neural eigenspace to impose consistency on predictions between related images. Comprehensive experiments and ablation studies verify the proposed approach, which demonstrates superiority over others; it is even comparable to some full-label supervised ones and works well when scribbles are randomly shrunk or dropped.