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Poster
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Jian Wang · Chenhui Gou · Qiman Wu · Haocheng Feng · Junyu Han · Errui Ding · Jingdong Wang

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #635
in Poster Session 3 »

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.

[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Jian Wang (Baidu)
Chenhui Gou (Australian National University)
Qiman Wu (National Pedagogical University M. Dragomanov)
Haocheng Feng (Baidu)
Junyu Han (Baidu)
Errui Ding (Baidu Inc.)
Jingdong Wang (Microsoft)

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