# Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?

arXiv: https://arxiv.org/abs/2203.13427 · HTML: https://mlsanity.com/paper/2203.13427/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 61 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring](https://mlsanity.com/paper/1911.09785/) (2019)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)

## Built on by (most cited first, 21 of 21)

- [The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation](https://mlsanity.com/paper/2303.15062/) (2023)
- [SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement](https://mlsanity.com/paper/2502.06756/) (2025)
- [Pseudo-label Alignment for Semi-supervised Instance Segmentation](https://mlsanity.com/paper/2308.05359/) (2023)
- [Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding](https://mlsanity.com/paper/2211.03850/) (2022)
- [Guided Distillation for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2308.02668/) (2023)
- [Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt](https://mlsanity.com/paper/2302.01171/) (2023)
- [TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation](https://mlsanity.com/paper/2211.09620/) (2022)
- [Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2402.04756/) (2024)
- [S^4M: Boosting Semi-Supervised Instance Segmentation with SAM](https://mlsanity.com/paper/2504.05301/) (2025)
- [Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation](https://mlsanity.com/paper/2405.13388/) (2024)
- [Extreme Point Supervised Instance Segmentation](https://mlsanity.com/paper/2405.20729/) (2024)
- [X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary Correction](https://mlsanity.com/paper/2309.08424/) (2023)
- [Boosting Box-supervised Instance Segmentation with Pseudo Depth](https://mlsanity.com/paper/2403.01214/) (2024)
- [Generalized Class Discovery in Instance Segmentation](https://mlsanity.com/paper/2502.08149/) (2025)
- [Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2505.11075/) (2025)
- [Better (pseudo-)labels for semi-supervised instance segmentation](https://mlsanity.com/paper/2403.11675/) (2024)
- [Depth-Guided Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2406.17413/) (2024)
- [Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation](https://mlsanity.com/paper/2607.29059/) (2026)
- [Unsupervised Instance Segmentation with Superpixels](https://mlsanity.com/paper/2509.05352/) (2025)
- [Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning](https://mlsanity.com/paper/2601.06642/) (2026)
- [Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting](https://mlsanity.com/paper/2603.16241/) (2026)

## Abstract (arXiv)

Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally address semi-supervised instance segmentation, where unlabeled images are employed to boost the performance. We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels. Under this framework, we point out that noisy boundaries associated with pseudo labels are double-edged. We propose to exploit and resist them in a unified manner simultaneously: 1) To combat the negative effects of noisy boundaries, we propose a noise-tolerant mask head by leveraging low-resolution features. 2) To enhance the positive impacts, we introduce a boundary-preserving map for learning detailed information within boundary-relevant regions. We evaluate our approach by extensive experiments. It behaves extraordinarily, outperforming the supervised baseline by a large margin, more than 6% on Cityscapes, 7% on COCO and 4.5% on BDD100k. On Cityscapes, our method achieves comparable performance by utilizing only 30% labeled images.