# Pseudo-label Alignment for Semi-supervised Instance Segmentation

arXiv: https://arxiv.org/abs/2308.05359 · HTML: https://mlsanity.com/paper/2308.05359/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 56 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [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)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [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)
- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [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)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [Learning with Pseudo-Ensembles](https://mlsanity.com/paper/1412.4864/) (2014)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [CenterMask : Real-Time Anchor-Free Instance Segmentation](https://mlsanity.com/paper/1911.06667/) (2019)
- [Data Distillation: Towards Omni-Supervised Learning](https://mlsanity.com/paper/1712.04440/) (2017)

## Built on by (most cited first, 17 of 17)

- [CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection](https://mlsanity.com/paper/2408.08050/) (2024)
- [3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting](https://mlsanity.com/paper/2404.17273/) (2024)
- [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)
- [ST-SAM: SAM-Driven Self-Training Framework for Semi-Supervised Camouflaged Object Detection](https://mlsanity.com/paper/2507.23307/) (2025)
- [Extreme Point Supervised Instance Segmentation](https://mlsanity.com/paper/2405.20729/) (2024)
- [DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation](https://mlsanity.com/paper/2507.10118/) (2025)
- [Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2505.11075/) (2025)
- [Depth-Guided Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2406.17413/) (2024)
- [BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2510.16863/) (2025)
- [Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentation](https://mlsanity.com/paper/2410.16063/) (2024)
- [CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation](https://mlsanity.com/paper/2505.21904/) (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)
- [HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling](https://mlsanity.com/paper/2603.21583/) (2026)
- [Training a Student Expert via Semi-Supervised Foundation Model Distillation](https://mlsanity.com/paper/2604.03841/) (2026)
- [iFAN: Inference-Aware Learning for Plain Mask Transformers](https://mlsanity.com/paper/2608.03216/) (2026)

## Abstract (arXiv)

Pseudo-labeling is significant for semi-supervised instance segmentation, which generates instance masks and classes from unannotated images for subsequent training. However, in existing pipelines, pseudo-labels that contain valuable information may be directly filtered out due to mismatches in class and mask quality. To address this issue, we propose a novel framework, called pseudo-label aligning instance segmentation (PAIS), in this paper. In PAIS, we devise a dynamic aligning loss (DALoss) that adjusts the weights of semi-supervised loss terms with varying class and mask score pairs. Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited. Notably, with just 1% labeled data, PAIS achieves 21.2 mAP (based on Mask-RCNN) and 19.9 mAP (based on K-Net) on the COCO dataset, outperforming the current state-of-the-art model, ie, NoisyBoundary with 7.7 mAP, by a margin of over 12 points. Code is available at: urlhttps://github.com/hujiecpp/PAIS.