Context and Spatial Feature Calibration for Real-Time Semantic Segmentation

• Published in: IEEE transactions on image processing Vol. 32; pp. 5465 - 5477
• Main Authors: Li, Kaige, Geng, Qichuan, Wan, Maoxian, Cao, Xiaochun, Zhou, Zhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Calibration; Context; context feature calibration; Context modeling; Misalignment; Modules; Pixels; Real time; Real-time semantic segmentation; Real-time systems; Sampling; Semantic segmentation; Semantics; spatial feature calibration; Transformers
• DOI: 10.1109/TIP.2023.3318967
• ISSN: 1057-7149; 1941-0042

Context modeling or multi-level feature fusion methods have been proved to be effective in improving semantic segmentation performance. However, they are not specialized to deal with the problems of pixel-context mismatch and spatial feature misalignment, and the high computational complexity hinders their widespread application in real-time scenarios. In this work, we propose a lightweight Context and Spatial Feature Calibration Network (CSFCN) to address the above issues with pooling-based and sampling-based attention mechanisms. CSFCN contains two core modules: Context Feature Calibration (CFC) module and Spatial Feature Calibration (SFC) module. CFC adopts a cascaded pyramid pooling module to efficiently capture nested contexts, and then aggregates private contexts for each pixel based on pixel-context similarity to realize context feature calibration. SFC splits features into multiple groups of sub-features along the channel dimension and propagates sub-features therein by the learnable sampling to achieve spatial feature calibration. Extensive experiments on the Cityscapes and CamVid datasets illustrate that our method achieves a state-of-the-art trade-off between speed and accuracy. Concretely, our method achieves 78.7% mIoU with 70.0 FPS and 77.8% mIoU with 179.2 FPS on the Cityscapes and CamVid test sets, respectively. The code is available at https://nave.vr3i.com/ and https://github.com/kaigelee/CSFCN .