Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation

Autor:	Stillger, Felix, Hasecke, Frederik, Meisen, Tobias
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: This is a technical report for a submission to the CVPR "SyntaGen - Harnessing Generative Models for Synthetic Visual Datasets" workshop challenge. The report is already uploaded to the workshop's homepage https://syntagen.github.io/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.17541 Zobrazit plný text záznamu View this record from Arxiv