G4Seg: Generation for Online Segmentation Refinement with Diffusion Models

Tianjiao Zhang, Fei Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang

Keywords: Diffusion models, Inexact Segmentation, Semantic Correspondence

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 paradigm 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.

Primary Area: generative models

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Submission Number: 6302