Compound Figure Separation

Compound figure separation focuses on isolating individual objects or components from complex scenes, whether in 3D models, generated images, or biomedical images. Current research explores both supervised and self-supervised learning approaches, employing techniques like implicit neural fields, diffusion models, and contrastive learning, often incorporating novel loss functions to improve object segmentation and reduce overlap. This work is significant for advancing 3D reconstruction, improving text-to-image generation, and enabling efficient use of large, unlabeled datasets in biomedical image analysis, ultimately leading to more robust and accurate AI models across various domains.