Recent real-time semantic segmentation methods usually adopt an additional
semantic branch to pursue rich long-range context. However, the additional
branch incurs undesirable computational overhead and slows inference speed. To
eliminate this dilemma, we propose SCTNet, a single branch CNN with transformer
semantic information for real-time segmentation. SCTNet enjoys the rich
semantic representations of an inference-free semantic branch while retaining
the high efficiency of lightweight single branch CNN. SCTNet utilizes a
transformer as the training-only semantic branch considering its superb ability
to extract long-range context. With the help of the proposed transformer-like
CNN block CFBlock and the semantic information alignment module, SCTNet could
capture the rich semantic information from the transformer branch in training.
During the inference, only the single branch CNN needs to be deployed. We
conduct extensive experiments on Cityscapes, ADE20K, and COCO-Stuff-10K, and
the results show that our method achieves the new state-of-the-art performance.
The code and model is available at this https URL

SCTNet 是一种采用变换器语义信息的单分支卷积神经网络，用于实时分割任务，并在 Cityscapes、ADE20K 和 COCO-Stuff-10K 数据集上展现出最新的最先进性能。