Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models

• URL: http://arxiv.org/abs/2506.10634v1
• Date: Thu, 12 Jun 2025 12:19:28 GMT
• Title: Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models
• Authors: Francisco Caetano, Christiaan Viviers, Peter H. N. De With, Fons van der Sommen,
• Abstract summary: Flow Matching has emerged as a powerful framework for learning continuous transformations between distributions.<n>This work introduces Symmetrical Flow Matching, a new formulation that unifies semantic segmentation, classification, and image generation within a single model.<n>It achieves state-of-the-art performance on semantic image synthesis, obtaining FID scores of 11.9 on CelebAMask-HQ and 7.0 on COCO-Stuff with only 25 inference steps.
• Score: 7.561148568365396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow Matching has emerged as a powerful framework for learning continuous transformations between distributions, enabling high-fidelity generative modeling. This work introduces Symmetrical Flow Matching (SymmFlow), a new formulation that unifies semantic segmentation, classification, and image generation within a single model. Using a symmetric learning objective, SymmFlow models forward and reverse transformations jointly, ensuring bi-directional consistency, while preserving sufficient entropy for generative diversity. A new training objective is introduced to explicitly retain semantic information across flows, featuring efficient sampling while preserving semantic structure, allowing for one-step segmentation and classification without iterative refinement. Unlike previous approaches that impose strict one-to-one mapping between masks and images, SymmFlow generalizes to flexible conditioning, supporting both pixel-level and image-level class labels. Experimental results on various benchmarks demonstrate that SymmFlow achieves state-of-the-art performance on semantic image synthesis, obtaining FID scores of 11.9 on CelebAMask-HQ and 7.0 on COCO-Stuff with only 25 inference steps. Additionally, it delivers competitive results on semantic segmentation and shows promising capabilities in classification tasks. The code will be publicly available.

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