Object Segmentation Without Labels with Large-Scale Generative Models

• URL: http://arxiv.org/abs/2006.04988v2
• Date: Fri, 11 Jun 2021 09:49:40 GMT
• Title: Object Segmentation Without Labels with Large-Scale Generative Models
• Authors: Andrey Voynov, Stanislav Morozov, Artem Babenko
• Abstract summary: Recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data. Large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling. We show that recent unsupervised GANs allow to differentiate between foreground/background pixels, providing high-quality saliency masks.
• Score: 43.679717400251924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent works employed these representations in a fully unsupervised setup for image classification, reducing the need for human labels on the fine-tuning stage as well. This work demonstrates that large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling. Namely, we show that recent unsupervised GANs allow to differentiate between foreground/background pixels, providing high-quality saliency masks. By extensive comparison on standard benchmarks, we outperform existing unsupervised alternatives for object segmentation, achieving new state-of-the-art.

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