2020
CVPR
CVPR 2020
High-Resolution Daytime Translation Without Domain Labels
Abstract
Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Importantly, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available.
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Keyword Pioneer
— image-to-image model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Application Areas > Domain Adaptation
Computer Vision > Generation > Image Translation
Computer Vision > Processing > Image Processing
Deep Learning > Learning Types > Adversarial Learning
Deep Learning > Learning Types > Generative Models