Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation
This addresses the challenge of creating labeled datasets for computer vision tasks like semantic segmentation, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of generating synthetic datasets for semantic segmentation by developing a method that extracts class-agnostic segmentation masks directly from Stable Diffusion latents using self-attentions and classifies them with cross-attentions, eliminating the need for additional segmentation-trained models.
This technical report outlines our method for generating a synthetic dataset for semantic segmentation using a latent diffusion model. Our approach eliminates the need for additional models specifically trained on segmentation data and is part of our submission to the CVPR 2024 workshop challenge, entitled CVPR 2024 workshop challenge "SyntaGen Harnessing Generative Models for Synthetic Visual Datasets". Our methodology uses self-attentions to facilitate a novel head-wise semantic information condensation, thereby enabling the direct acquisition of class-agnostic image segmentation from the Stable Diffusion latents. Furthermore, we employ non-prompt-influencing cross-attentions from text to pixel, thus facilitating the classification of the previously generated masks. Finally, we propose a mask refinement step by using only the output image by Stable Diffusion.