Coloring with limited data: Few-shot colorization via memory augmented networks

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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. Also, we propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need for class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.
Publisher
IEEE Computer Society
Issue Date
2019-06
Language
English
Citation

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp.11275 - 11284

DOI
10.1109/CVPR.2019.01154
URI
http://hdl.handle.net/10203/279876
Appears in Collection
RIMS Conference Papers
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