Adapts SwiftNet into SFN with ERFNet/GCNet-inspired blocks and compares loss functions, classifiers, and decision rules to increase recall in real-time semantic segmentation on CamVid and Cityscapes.
In defense of pre-trained imagenet architectures for real- time semantic segmentation of road-driving images,
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A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized Network
Adapts SwiftNet into SFN with ERFNet/GCNet-inspired blocks and compares loss functions, classifiers, and decision rules to increase recall in real-time semantic segmentation on CamVid and Cityscapes.