From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation

• URL: http://arxiv.org/abs/2605.04590v1
• Date: Wed, 06 May 2026 07:40:45 GMT
• Title: From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation
• Abstract summary: Text-based image segmentation aims to delineate object boundaries within an image from text prompts.<n>Recent studies have shown that diffusion models can provide rich multimodal semantic features.<n>We propose RLFSeg, a novel framework that leverages Rectified Flow to learn direct mapping from the image to the segmentation mask within the latent space.
• Score: 6.365160557699766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-based image segmentation aims to delineate object boundaries within an image from text prompts, offering higher flexibility and broader application scope compared to traditional fixed-category segmentation tasks. Recent studies have shown that diffusion models (e.g., Stable Diffusion) can provide rich multimodal semantic features, leading to studies of using diffusion models as feature extractors for segmentation tasks. Such methods, however, inherit the generative natures of diffusion models that are harmful to discriminative segmentation tasks. In response, we propose RLFSeg, a novel framework that leverages Rectified Flow to learn direct mapping from the image to the segmentation mask within the latent space. The model is thus freed from the noise-denoise process and the need to optimize the time step of diffusion models, resulting in substantially better performance than previous diffusion-based methods, especially on zero-shot scenarios. By introducing label refinement and an Adaptive One-Step Sampling strategy, the model achieves higher accuracy even on a single inference step. The framework redirects a pretrained generative model to the discriminative segmentation task with zero modification to model structure, thus reveals promising application potential and significant research value.

Related papers

• Making Training-Free Diffusion Segmentors Scale with the Generative Power [118.72472901404814]
  A line of research focuses on adapting a pre-trained diffusion model to semantic segmentation without any further training.<n>We propose two techniques: auto aggregation and per-pixel rescaling, which together enable training-free segmentation to better leverage generative capability.
  arXiv  Detail & Related papers  (2026-03-06T11:35:37Z)
• GS: Generative Segmentation via Label Diffusion [59.380173266566715]
  Language-driven image segmentation is a fundamental task in vision-language understanding, requiring models to segment regions of an image corresponding to natural language expressions.<n>Recent diffusion models have been introduced to this domain, but existing approaches remain image-centric.<n>We propose GS (Generative Label), a novel framework that formulates segmentation itself as a generative task.<n> Experimental results show that GS significantly outperforms existing discriminative and diffusion-based methods, setting a new state-of-the-art for language-driven segmentation.
  arXiv  Detail & Related papers  (2025-08-27T16:28:15Z)
• G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models [38.44872934965588]
  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.
  arXiv  Detail & Related papers  (2025-06-02T11:05:28Z)
• Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation [56.87049651707208]
  Few-shot Semantic has evolved into In-context tasks, morphing into a crucial element in assessing generalist segmentation models. Our initial focus lies in understanding how to facilitate interaction between the query image and the support image, resulting in the proposal of a KV fusion method within the self-attention framework. Based on our analysis, we establish a simple and effective framework named DiffewS, maximally retaining the original Latent Diffusion Model's generative framework.
  arXiv  Detail & Related papers  (2024-10-03T10:33:49Z)
• Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption [73.98706049140098]
  We propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss. Specifically, we design a phasic training strategy with phasic content fusion to help our model learn content and style information when t is large. Finally, we propose a cross-domain structure guidance strategy that enhances structure consistency during domain adaptation.
  arXiv  Detail & Related papers  (2023-09-07T14:14:11Z)
• Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter [47.29967666846132]
  generative text-to-image diffusion models are highly efficient open-vocabulary semantic segmenters. We introduce a novel training-free approach named DiffSegmenter to generate realistic objects that are semantically faithful to the input text. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation.
  arXiv  Detail & Related papers  (2023-09-06T06:31:08Z)
• Prompting Diffusion Representations for Cross-Domain Semantic Segmentation [101.04326113360342]
  diffusion-pretraining achieves extraordinary domain generalization results for semantic segmentation. We introduce a scene prompt and a prompt randomization strategy to help further disentangle the domain-invariant information when training the segmentation head.
  arXiv  Detail & Related papers  (2023-07-05T09:28:25Z)
• Denoising Diffusion Semantic Segmentation with Mask Prior Modeling [61.73352242029671]
  We propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a denoising diffusion generative model. We evaluate the proposed prior modeling with several off-the-shelf segmentors, and our experimental results on ADE20K and Cityscapes demonstrate that our approach could achieve competitively quantitative performance.
  arXiv  Detail & Related papers  (2023-06-02T17:47:01Z)
• Label-Efficient Semantic Segmentation with Diffusion Models [27.01899943738203]
  We demonstrate that diffusion models can also serve as an instrument for semantic segmentation. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem.
  arXiv  Detail & Related papers  (2021-12-06T15:55:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.