Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation

TL;DR

This paper presents a novel method for generating synthetic semantic segmentation datasets using a latent diffusion model with self-attention and cross-attention mechanisms, eliminating the need for additional segmentation-specific models.

Contribution

The approach introduces a head-wise semantic information condensation and cross-attention techniques to directly obtain class-agnostic segmentation masks from diffusion model latents.

Findings

Effective generation of semantic segmentation masks from diffusion model latents

Elimination of additional segmentation-specific training models

Improved mask quality through a refinement step

Abstract

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.