Context and Spatial Feature Calibration for Real-Time Semantic Segmentation

Area of Science:

• Computer Vision
• Deep Learning
• Image Segmentation

Background:

• Existing context modeling and feature fusion methods improve semantic segmentation but struggle with pixel-context mismatch and spatial misalignment.
• High computational complexity limits the real-time application of current semantic segmentation techniques.

Purpose of the Study:

• To propose a lightweight Context and Spatial Feature Calibration Network (CSFCN) for efficient and accurate semantic segmentation.
• To address pixel-context mismatch and spatial feature misalignment issues in semantic segmentation.
• To achieve a state-of-the-art trade-off between speed and accuracy for real-time scenarios.

Main Methods:

• Developed a lightweight CSFCN utilizing pooling-based and sampling-based attention mechanisms.
• Introduced a Context Feature Calibration (CFC) module with cascaded pyramid pooling for nested context capture and pixel-context similarity aggregation.
• Implemented a Spatial Feature Calibration (SFC) module that splits features and uses learnable sampling for spatial alignment.

Main Results:

• Achieved state-of-the-art performance on Cityscapes and CamVid datasets, demonstrating an excellent speed-accuracy trade-off.
• Attained 78.7% mIoU at 70.0 FPS on the Cityscapes test set.
• Achieved 77.8% mIoU at 179.2 FPS on the CamVid test set.

Conclusions:

• The proposed CSFCN effectively calibrates context and spatial features, overcoming limitations of previous methods.
• CSFCN offers a computationally efficient solution for real-time semantic segmentation without compromising accuracy.
• The method demonstrates significant improvements in both speed and accuracy for semantic segmentation tasks.