SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation

• URL: http://arxiv.org/abs/2301.00366v3
• Date: Tue, 4 Apr 2023 18:44:03 GMT
• Title: SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
• Authors: Kunal Chaturvedi, Ali Braytee, Jun Li, Mukesh Prasad
• Abstract summary: This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.
• Score: 8.683844587821918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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