A 14-step additive ablation recipe plus a ResNet max-pooling removal in DeepLabV3+ raises semantic segmentation mIoU on low-resolution damaged-road datasets RTK and TAS500 to claimed state-of-the-art values.
Advances in Neural Information Processing Systems34, 22614–22627 (2021)
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A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads
A 14-step additive ablation recipe plus a ResNet max-pooling removal in DeepLabV3+ raises semantic segmentation mIoU on low-resolution damaged-road datasets RTK and TAS500 to claimed state-of-the-art values.