# Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation

Source: [Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation](<https://www.sota2.com/research/paper/68fa73e21e209131cea5eabd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:58:03.881Z

Authors: Felix Stillger, Frederik Hasecke, Tobias Meisen

Published: 2024-06-25T13:28:53.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on PASCAL VOC (val)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-voc-val.md>): Semantic segmentation; PASCAL VOC (val); mIoU: 46.25.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.17541>)

[PDF](<https://arxiv.org/pdf/2406.17541.pdf>)