Sketch-to-image is an important task to reduce the burden of creating a color image from scratch. Unlike previous sketch-to-image models, where the image is synthesized in an end-to-end manner, leading to an unnaturalistic image, we propose a method by decomposing the problem into subproblems to generate a more naturalistic and reasonable image. It first generates an intermediate output which is a semantic mask map from the input sketch through instance and semantic segmentation in two levels, background segmentation and foreground segmentation. Background segmentation is formed based on the context of the foreground objects. Then, the foreground segmentations are sequentially added to the created background segmentation. Finally, the generated mask map is fed into an image-to-image translation model to generate an image. Our proposed method works with 92 distinct classes. Compared to state-of-the-art sketch-to-image models, our proposed method outperforms the previous methods and generates better images.
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Unsupervised foreground extraction via deep region competition.
We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner. Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background. In this work, we rethink the foreground extraction by reconciling energy-based prior with generative image modeling in the form of Mixture of Experts (MoE), where we further introduce the learned pixel re-assignment as the essential inductive bias to capture the regularities of background regions. With this modeling, the foreground/background partition can be naturally found through Expectation-Maximization
(EM). We show that the proposed method effectively exploits the interaction between the mixture components during the partitioning process, which closely connects to region competition, a seminal approach for generic image segmentation. Experiments demonstrate that DRC exhibits more competitive performances on complex real-world data and challenging multi-object scenes compared with prior methods. Moreover, we show empirically that DRC can potentially generalize to novel foreground objects even from categories unseen during training.
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- Award ID(s):
- 2015577
- NSF-PAR ID:
- 10351314
- Date Published:
- Journal Name:
- Neural Information Processing Systems (NeurIPS 2021).
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
