Golden Cudgel Network for Real-Time Semantic Segmentation
Guoyu Yang, Yuan Wang, Daming Shi, Yanzhong Wang
Abstract
Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GCNet uses vertical multi-convolutions and horizontal multi-paths for training, which are reparameterized into a single convolution for inference, optimizing both performance and speed. This design allows GCNet to self-enlarge during training and self-contract during inference, effectively becoming a "teacher model" without needing external ones. Experimental results show that GCNet outperforms existing state-of-the-art models in terms of performance and speed on the Cityscapes, CamVid, and Pascal VOC 2012 datasets. The code is available at https://github.com/gyyang23/GCNet.
BibTeX
@InProceedings{Yang_2025_CVPR,
author = {Yang, Guoyu and Wang, Yuan and Shi, Daming and Wang, Yanzhong},
title = {Golden Cudgel Network for Real-Time Semantic Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25367-25376}
}