SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation
This work addresses the efficiency problem for real-time semantic segmentation applications by eliminating the need for an extra branch during inference, though it is incremental as it builds on existing CNN and transformer methods.
The authors tackled the computational overhead of using an additional semantic branch in real-time semantic segmentation by proposing SCTNet, a single-branch CNN that integrates transformer semantic information during training only, achieving new state-of-the-art performance on Cityscapes, ADE20K, and COCO-Stuff-10K datasets.
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 https://github.com/xzz777/SCTNet