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

• URL: http://arxiv.org/abs/2210.07124v1
• Date: Thu, 13 Oct 2022 16:03:53 GMT
• Title: RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
• Authors: Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang
• Abstract summary: We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation. It achieves better trade-off between performance and efficiency than CNN-based models. Experiments on mainstream benchmarks demonstrate the effectiveness of our proposed RTFormer.
• Score: 63.25665813125223
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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