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Self-Supervised Object Segmentation with a Cut-and-Pasting GAN

2023-01-01 07:42:50
Kunal Chaturvedi, Ali Braytee, Jun Li, Mukesh Prasad

Abstract

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2301.00366

PDF

https://arxiv.org/pdf/2301.00366.pdf


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