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

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dc.contributor.authorYoo, Seungjooko
dc.contributor.authorBahng, Hyojinko
dc.contributor.authorChung, Sunghyoko
dc.contributor.authorLee, Junsooko
dc.contributor.authorChang, Jaehyukko
dc.contributor.authorChoo, Jaegulko
dc.identifier.citation32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp.11275 - 11284-
dc.description.abstractDespite 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.-
dc.publisherIEEE Computer Society-
dc.titleColoring with limited data: Few-shot colorization via memory augmented networks-
dc.citation.publicationname32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019-
dc.identifier.conferencelocationLong Beach-
dc.contributor.localauthorChoo, Jaegul-
dc.contributor.nonIdAuthorYoo, Seungjoo-
dc.contributor.nonIdAuthorBahng, Hyojin-
dc.contributor.nonIdAuthorChung, Sunghyo-
dc.contributor.nonIdAuthorLee, Junsoo-
dc.contributor.nonIdAuthorChang, Jaehyuk-
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