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

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Automated foreground object segmentation and realistic image compositing are challenging tasks in computer vision.
• Existing methods often require extensive manual annotations, limiting their scalability and efficiency.

Purpose of the Study:

• To propose a novel self-supervised approach for foreground object segmentation and realistic image generation.
• To eliminate the need for manual annotations in the segmentation and compositing process.
• To enhance the learning capabilities of Generative Adversarial Networks (GANs) for improved image synthesis.

Main Methods:

• A self-supervised Cut-and-Paste Generative Adversarial Network (GAN) framework is proposed.
• A U-Net discriminator is employed to learn both global (real/fake classification) and local (semantic/structural) image representations.
• Pseudo-labels generated via a self-supervised task guide the discriminator to extract richer image information.

Main Results:

• The proposed method successfully performs foreground object segmentation and generates realistic composite images.
• The U-Net discriminator effectively learns semantic and structural information, improving the generator's output.
• Experimental results show significant performance improvements over state-of-the-art methods on benchmark datasets.

Conclusions:

• The self-supervised Cut-and-Paste GAN offers an effective annotation-free solution for foreground segmentation and image generation.
• Integrating semantic and structural learning into the discriminator enhances the quality of generated images.
• The proposed approach represents a significant advancement in unsupervised image synthesis and segmentation.