Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909231190376448 |
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| author | Stillger, Felix Hasecke, Frederik Meisen, Tobias |
| author_facet | Stillger, Felix Hasecke, Frederik Meisen, Tobias |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_17541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation Stillger, Felix Hasecke, Frederik Meisen, Tobias Computer Vision and Pattern Recognition 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. |
| title | Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.17541 |