# MP-Former: Mask-Piloted Transformer for Image Segmentation

arXiv: https://arxiv.org/abs/2303.07336 · HTML: https://mlsanity.com/paper/2303.07336/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 40 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)

## Built on by (most cited first, 30 of 39)

- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Frequency-Spatial Entanglement Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2409.01686/) (2024)
- [BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge](https://mlsanity.com/paper/2308.10175/) (2023)
- [VST++: Efficient and Stronger Visual Saliency Transformer](https://mlsanity.com/paper/2310.11725/) (2023)
- [Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation Masks](https://mlsanity.com/paper/2407.13517/) (2024)
- [Textual Query-Driven Mask Transformer for Domain Generalized Segmentation](https://mlsanity.com/paper/2407.09033/) (2024)
- [Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation](https://mlsanity.com/paper/2407.13362/) (2024)
- [UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation](https://mlsanity.com/paper/2410.09909/) (2024)
- [Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation](https://mlsanity.com/paper/2311.17626/) (2023)
- [CPM: Class-conditional Prompting Machine for Audio-visual Segmentation](https://mlsanity.com/paper/2407.05358/) (2024)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [detrex: Benchmarking Detection Transformers](https://mlsanity.com/paper/2306.07265/) (2023)
- [LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery](https://mlsanity.com/paper/2406.16502/) (2024)
- [Rethinking Remote Sensing Change Detection With A Mask View](https://mlsanity.com/paper/2406.15320/) (2024)
- [SymPoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement](https://mlsanity.com/paper/2407.01928/) (2024)
- [LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning](https://mlsanity.com/paper/2407.13217/) (2024)
- [Boltzmann Attention Sampling for Image Analysis with Small Objects](https://mlsanity.com/paper/2503.02841/) (2025)
- [DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning](https://mlsanity.com/paper/2504.04085/) (2025)
- [LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2303.07747/) (2023)
- [GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models](https://mlsanity.com/paper/2307.12018/) (2023)
- [Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer](https://mlsanity.com/paper/2404.03819/) (2024)
- [Frequency-based Matcher for Long-tailed Semantic Segmentation](https://mlsanity.com/paper/2406.03917/) (2024)
- [DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model](https://mlsanity.com/paper/2410.16707/) (2024)
- [MaSS13K: A Matting-level Semantic Segmentation Benchmark](https://mlsanity.com/paper/2503.18364/) (2025)
- [Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models](https://mlsanity.com/paper/2508.01667/) (2025)
- [IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks](https://mlsanity.com/paper/2412.16654/) (2024)
- [Synthetic Instance Segmentation from Semantic Image Segmentation Masks](https://mlsanity.com/paper/2308.00949/) (2023)

## Abstract (arXiv)

We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation. The improvement is based on our observation that Mask2Former suffers from inconsistent mask predictions between consecutive decoder layers, which leads to inconsistent optimization goals and low utilization of decoder queries. To address this problem, we propose a mask-piloted training approach, which additionally feeds noised ground-truth masks in masked-attention and trains the model to reconstruct the original ones. Compared with the predicted masks used in mask-attention, the ground-truth masks serve as a pilot and effectively alleviate the negative impact of inaccurate mask predictions in Mask2Former. Based on this technique, our M achieves a remarkable performance improvement on all three image segmentation tasks (instance, panoptic, and semantic), yielding +2.3AP and +1.6mIoU on the Cityscapes instance and semantic segmentation tasks with a ResNet-50 backbone. Our method also significantly speeds up the training, outperforming Mask2Former with half of the number of training epochs on ADE20K with both a ResNet-50 and a Swin-L backbones. Moreover, our method only introduces little computation during training and no extra computation during inference. Our code will be released at urlhttps://github.com/IDEA-Research/MP-Former.