G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912409455689728 |
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| author | Zhang, Tianjiao Zhang, Fei Yao, Jiangchao Zhang, Ya Wang, Yanfeng |
| author_facet | Zhang, Tianjiao Zhang, Fei Yao, Jiangchao Zhang, Ya Wang, Yanfeng |
| contents | This paper considers the problem of utilizing a large-scale text-to-image diffusion model to tackle the challenging Inexact Segmentation (IS) task. Unlike traditional approaches that rely heavily on discriminative-model-based paradigms or dense visual representations derived from internal attention mechanisms, our method focuses on the intrinsic generative priors in Stable Diffusion~(SD). Specifically, we exploit the pattern discrepancies between original images and mask-conditional generated images to facilitate a coarse-to-fine segmentation refinement by establishing a semantic correspondence alignment and updating the foreground probability. Comprehensive quantitative and qualitative experiments validate the effectiveness and superiority of our plug-and-play design, underscoring the potential of leveraging generation discrepancies to model dense representations and encouraging further exploration of generative approaches for solving discriminative tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Zhang, Tianjiao Zhang, Fei Yao, Jiangchao Zhang, Ya Wang, Yanfeng Computer Vision and Pattern Recognition Artificial Intelligence This paper considers the problem of utilizing a large-scale text-to-image diffusion model to tackle the challenging Inexact Segmentation (IS) task. Unlike traditional approaches that rely heavily on discriminative-model-based paradigms or dense visual representations derived from internal attention mechanisms, our method focuses on the intrinsic generative priors in Stable Diffusion~(SD). Specifically, we exploit the pattern discrepancies between original images and mask-conditional generated images to facilitate a coarse-to-fine segmentation refinement by establishing a semantic correspondence alignment and updating the foreground probability. Comprehensive quantitative and qualitative experiments validate the effectiveness and superiority of our plug-and-play design, underscoring the potential of leveraging generation discrepancies to model dense representations and encouraging further exploration of generative approaches for solving discriminative tasks. |
| title | G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.01539 |