  Despite recent advancements in deep learning-based automatic colorization,
they are still limited when it comes to few-shot learning. Existing models
require a significant amount of training data. To tackle this issue, we present
a novel memory-augmented colorization model MemoPainter that can produce
high-quality colorization with limited data. In particular, our model is able
to capture rare instances and successfully colorize them. We also propose a
novel threshold triplet loss that enables unsupervised training of memory
networks without the need of class labels. Experiments show that our model has
superior quality in both few-shot and one-shot colorization tasks.
