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Foreground-Aware Semantic Representations for Image Harmonizationстатья
- Авторы: Sofiiuk Konstantin, Popenova Polina, Konushin Anton
- Сборник: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Год издания: 2021
- Первая страница: 1620
- Последняя страница: 1629
- Аннотация: Image harmonization is an important step in photo editing to achieve visual consistency in composite images by adjusting the appearances of a foreground to make it compatible with a background. Previous approaches to harmonize composites are based on training of encoder-decoder networks from scratch, which makes it challenging for a neural network to learn a high-level representation of objects. We propose a novel architecture to utilize the spaceof high-level features learned by a pre-trained classification network. We create our models as a combination of existing encoder-decoder architectures and a pre-trained foreground-aware deep high-resolution network. We extensively evaluate the proposed method on the existing imageharmonization benchmark and set up a new state-of-the-art in terms of MSE and PSNR metrics. The code and trained models are available publicly.
- Добавил в систему: Конушин Антон Сергеевич