G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

• URL: http://arxiv.org/abs/2506.01539v1
• Date: Mon, 02 Jun 2025 11:05:28 GMT
• Title: G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models
• Authors: Tianjiao Zhang, Fei Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang,
• Abstract summary: This paper considers the problem of utilizing a large-scale text-to-image model to tackle the Inexact diffusion (IS) task.<n>We exploit the pattern discrepancies between original images and mask-conditional generated images to facilitate a coarse-to-fine segmentation refinement.
• Score: 38.44872934965588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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