# Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation

arXiv: https://arxiv.org/abs/2412.17331 · HTML: https://mlsanity.com/paper/2412.17331/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 41 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels](https://mlsanity.com/paper/2203.03884/) (2022)
- [ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2106.05095/) (2021)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Rethinking Semantic Segmentation: A Prototype View](https://mlsanity.com/paper/2203.15102/) (2022)
- [Cross-Image Relational Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2204.06986/) (2022)
- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2111.12903/) (2021)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2212.04976/) (2022)
- [Semi-supervised Semantic Segmentation with Error Localization Network](https://mlsanity.com/paper/2204.02078/) (2022)
- [CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2306.04300/) (2023)
- [Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2303.01276/) (2023)
- [Generative Semantic Segmentation](https://mlsanity.com/paper/2303.11316/) (2023)
- [Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2206.09554/) (2022)
- [Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2310.09760/) (2023)
- [Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05154/) (2023)
- [Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2211.16701/) (2022)
- [Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2407.10649/) (2024)
- [Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation](https://mlsanity.com/paper/2208.11499/) (2022)
- [Contextrast: Contextual Contrastive Learning for Semantic Segmentation](https://mlsanity.com/paper/2404.10633/) (2024)
- [Fully Attentional Network for Semantic Segmentation](https://mlsanity.com/paper/2112.04108/) (2021)
- [Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2310.09828/) (2023)

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

- [PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation](https://mlsanity.com/paper/2603.17520/) (2026)
- [Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2503.02459/) (2025)
- [Scale-aware Adaptive Supervised Network with Limited Medical Annotations](https://mlsanity.com/paper/2601.01005/) (2026)
- [Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation](https://mlsanity.com/paper/2603.16340/) (2026)
- [BoundMatch: Boundary detection applied to semi-supervised segmentation](https://mlsanity.com/paper/2503.23519/) (2025)
- [DFEN: Dual Feature Equalization Network for Medical Image Segmentation](https://mlsanity.com/paper/2505.05913/) (2025)

## Abstract (arXiv)

Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data. However, existing consistency regularization methods only utilize high certain pixels with prediction confidence surpassing a fixed threshold for training, failing to fully leverage the potential supervisory information within the network. Therefore, this paper proposes the Uncertainty-participation Context Consistency Learning (UCCL) method to explore richer supervisory signals. Specifically, we first design the semantic backpropagation update (SBU) strategy to fully exploit the knowledge from uncertain pixel regions, enabling the model to learn consistent pixel-level semantic information from those areas. Furthermore, we propose the class-aware knowledge regulation (CKR) module to facilitate the regulation of class-level semantic features across different augmented views, promoting consistent learning of class-level semantic information within the encoder. Experimental results on two public benchmarks demonstrate that our proposed method achieves state-of-the-art performance. Our code is available at https://github.com/YUKEKEJAN/UCCL.