This repository contains the official Pytorch implementation of training & evaluation code and the trained models for Offset Learning & OffSeg.
Offset Learning —— An efficient plug-and-play semantic segmentation paradigm that replaces existing per-pixel classification paradigm to boost performance with negligible parameters.
Overview of the Offset Learning framework for semantic segmentation.
Abstract
Offset Learning is a new semantic segmentation paradigm that efficiently learns feature offsets and class offsets to dynamically refine both spatial features and class representations, addressing the inherent misalignment problem in per-pixel classification. Based on this paradigm, we design OffSeg, an efficient segmentation network that delivers consistent accuracy improvements on multiple benchmarks. Notably, the Offset Learning paradigm is plug-and-play, allowing it to directly replace other segmentation paradigms in existing models to achieve performance gains with only negligible parameter overhead.
Features
Offset Learning: Learns feature offsets and class offsets to dynamically refine spatial features and class representations.
Plug-and-play: Compatible with existing segmentation frameworks like SegFormer, SegNeXt, and Mask2Former.
Lightweight & Efficient: Achieves consistent accuracy gains on multiple benchmarks with negligible parameter overhead.
Proven Effectiveness: Validated across diverse models and datasets, showing strong improvements especially in lightweight settings.
News
2025.09.06: The Chinese version has been updated for Chinese readers.
2025.08.13: Add tutorial on how to apply the Offset Learning paradigm to your own models.
2025.08.12: The full training & evaluation code & Jittor version code and the trained models are released.
2025.06.26: Our paper is accepted to ICCV 2025!
TODO
Release the full training & evaluation code and model weights.
Tutorial on how to apply the Offset Learning paradigm to your own models.
Release the jittor version for jittor users.
Release the Python library for easier installation via pip install.
Explore the generalization ability of Offset Learning on tasks beyond semantic segmentation.
If you find this work useful for your research, please cite our paper:
@article{zhang2025revisiting,
title={Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment},
author={Zhang, Shi-Chen and Li, Yunheng and Wu Yu-Huan and Hou, Qibin and Cheng, Ming-Ming},
journal={arXiv preprint arXiv:2508.08811},
year={2025}
}
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment (ICCV 2025)
Project page | Paper | arXiv | 中译版 | Jittor
This repository contains the official Pytorch implementation of training & evaluation code and the trained models for Offset Learning & OffSeg.
Offset Learning —— An efficient plug-and-play semantic segmentation paradigm that replaces existing per-pixel classification paradigm to boost performance with negligible parameters.
Abstract
Offset Learning is a new semantic segmentation paradigm that efficiently learns feature offsets and class offsets to dynamically refine both spatial features and class representations, addressing the inherent misalignment problem in per-pixel classification. Based on this paradigm, we design OffSeg, an efficient segmentation network that delivers consistent accuracy improvements on multiple benchmarks. Notably, the Offset Learning paradigm is plug-and-play, allowing it to directly replace other segmentation paradigms in existing models to achieve performance gains with only negligible parameter overhead.Features
News
2025.09.06: The Chinese version has been updated for Chinese readers.2025.08.13: Add tutorial on how to apply the Offset Learning paradigm to your own models.2025.08.12: The full training & evaluation code & Jittor version code and the trained models are released.2025.06.26: Our paper is accepted to ICCV 2025!TODO
pip install.Get Started
Installation
Data Preparation
For data preparation, please refer to the guidelines in mmsegmentation. It is recommended to symlink the dataset root to
OffSeg/data.For convenience, the recommended folder structure is as follows:
Checkpoints
The trained models can be downloaded at: | Model | GoogleDrive | OneDrive | BaiduNetdisk | |——————————–|————-|———-|————–| | OffSeg | GoogleDrive | OneDrive | BaiduNetdisk | | SegFormer w/ Offset Learning | GoogleDrive | OneDrive | BaiduNetdisk | | SegNeXt w/ Offset Learning | GoogleDrive | OneDrive | BaiduNetdisk | | Mask2Former w/ Offset Learning | GoogleDrive | OneDrive | BaiduNetdisk |
Evaluation
Single GPU Evaluation
Multi-GPU Evaluation
Evaluation with Visualization
Training
Single GPU Training
Multi-GPU Training
Visualization
Image Demo
Citation
If you find this work useful for your research, please cite our paper:
Acknowledgment
This project is built upon MMSegmentation. We thank the MMSegmentation team for their open-source contribution. We also thank the following open-source projects for their inspiring work: SegFormer, SegNeXt, Mask2Former, FreqFusion, EfficientFormerV2.
License
The code is limited to non-commercial, academic, or research purposes only. For commercial use, please contact the authors for licensing.
Contact
For questions and issues, please contact: