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Recent real-time semantic segmentation methods usually adopt an additionalsemantic branch to pursue rich long-range context. However, the additionalbranch incurs undesirable computational overhead and slows inference speed. Toeliminate this dilemma, we propose SCTNet, a single branch CNN with transformersemantic information for real-time segmentation. SCTNet enjoys the richsemantic representations of an inference-free semantic branch while retainingthe high efficiency of lightweight single branch CNN. SCTNet utilizes atransformer as the training-only semantic branch considering its superb abilityto extract long-range context. With the help of the proposed transformer-likeCNN block CFBlock and the semantic information alignment module, SCTNet couldcapture the rich semantic information from the transformer branch in training.During the inference, only the single branch CNN needs to be deployed. Weconduct extensive experiments on Cityscapes, ADE20K, and COCO-Stuff-10K, andthe results show that our method achieves the new state-of-the-art performance.The code and model is available at https://github.com/xzz777/SCTNet

SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation