  Recent studies indicate that deep learning plays a crucial role in the
automated visual inspection of road infrastructures. However, current learning
schemes are static, implying no dynamic adaptation to users' feedback. To
address this drawback, we present a few-shot learning paradigm for the
automated segmentation of road cracks, which is based on a U-Net architecture
with recurrent residual and attention modules (R2AU-Net). The retraining
strategy dynamically fine-tunes the weights of the U-Net as a few new rectified
samples are being fed into the classifier. Extensive experiments show that the
proposed few-shot R2AU-Net framework outperforms other state-of-the-art
networks in terms of Dice and IoU metrics, on a new dataset, named CrackMap,
which is made publicly available at https://github.com/ikatsamenis/CrackMap.
