SS-CPGAN：用于目标分割的自监督剪切与粘贴生成对抗网络
SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
AI总结：
本文提出SS-CPGAN，一种结合U-Net判别器的自监督剪切与粘贴生成对抗网络，无需人工标注即可实现前景目标分割与逼真图像合成，在标准基准数据集上显著优于现有最先进方法。
摘要：
本文提出了一种新颖的基于自监督的Cut-and-Paste GAN，可在无需人工标注的情况下执行前景目标分割并生成逼真的合成图像。我们通过一种简单而有效的自监督方法结合基于U-Net的判别器实现了这一目标。所提出的方法扩展了标准判别器的能力，使其不仅通过分类（真/假）学习全局数据表示，还通过使用自监督任务创建的伪标签学习语义和结构信息。该方法促使生成器通过从判别器学习有信息量的逐像素以及全局图像反馈，来创建有意义的掩码。我们的实验表明，在标准基准数据集上，所提出的方法显著优于现有最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
