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 Memo-Painter 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.
Despite remarkable success in deep learning-based facerelated models, these models are still limited to the domain of real human faces. On the other hand, the domain of animation faces has been studied less intensively due to the absence of a well-organized dataset. In this paper, we present a large-scale animation celebfaces dataset (Anime-Celeb) via controllable synthetic animation models to boost research on the animation face domain. To facilitate the data generation process, we build a semi-automatic pipeline based on an open 3D software and a developed annotation system. This leads to constructing a large-scale animation face dataset that includes multi-pose and multi-style animation faces with rich annotations. Experiments suggest that our dataset is applicable to various animation-related tasks * These authors contributed equally. such as head reenactment and colorization.
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