RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

TL;DR

RTFormer is a novel dual-resolution transformer designed for real-time semantic segmentation, offering a better balance of accuracy and efficiency than traditional CNN models by utilizing GPU-friendly attention and cross-resolution strategies.

Contribution

The paper introduces RTFormer, an efficient transformer architecture that reduces computational complexity and improves real-time segmentation performance compared to existing CNN-based methods.

Findings

Achieves state-of-the-art results on Cityscapes, CamVid, and COCOStuff benchmarks.

Demonstrates high inference efficiency on GPU-like devices.

Outperforms CNN-based models in the trade-off between accuracy and speed.

Abstract

Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of transformer. We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation, which achieves better trade-off between performance and efficiency than CNN-based models. To achieve high inference efficiency on GPU-like devices, our RTFormer leverages GPU-Friendly Attention with linear complexity and discards the multi-head mechanism. Besides, we find that cross-resolution attention is more efficient to gather global context information for high-resolution branch by spreading the high level knowledge learned from low-resolution branch. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our proposed RTFormer, it achieves state-of-the-art on Cityscapes, CamVid and COCOStuff, and shows promising results on ADE20K. Code is available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.