GS：通过标签扩散的生成式分割
GS: Generative Segmentation via Label Diffusion
AI总结：
本文提出GS框架，将语言驱动的图像分割重新定义为以图像和文本为条件的标签扩散生成任务，并在PNG基准上取得新的最先进性能。
摘要：
语言驱动的图像分割是视觉-语言理解中的一项基础任务，要求模型分割出图像中与自然语言表达相对应的区域。传统方法将其视为判别式问题，基于语义对齐将每个像素分配为前景或背景。近年来，扩散模型被引入该领域，但现有方法仍以图像为中心：它们要么（i）使用图像扩散模型作为视觉特征提取器，要么（ii）通过图像生成合成分割数据来训练判别式模型，要么（iii）执行扩散反演从预训练图像扩散模型中提取注意力线索——从而将分割视为辅助过程。本文提出GS（Generative Segmentation，生成式分割），一种新颖的框架，通过标签扩散将分割本身形式化为生成式任务。GS不生成以标签图和文本为条件的图像，而是反转生成过程：它直接从噪声中生成分割掩码，以输入图像和伴随的语言描述为条件。该范式使标签生成成为主要建模目标，实现端到端训练，并对空间和语义保真度进行显式控制。为证明方法的有效性，我们在Panoptic Narrative Grounding（PNG）上评估GS，该基准是多模态分割中具有代表性且富有挑战性的基准，要求由叙事字幕引导的全景级推理。实验结果表明，GS显著优于现有判别式和基于扩散的方法，为语言驱动的分割设立了新的最先进水平。
英文摘要：
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. Traditional methods approach this as a discriminative problem, assigning each pixel to foreground or background based on semantic alignment. Recently, diffusion models have been introduced to this domain, but existing approaches remain image-centric: they either (i) use image diffusion models as visual feature extractors, (ii) synthesize segmentation data via image generation to train discriminative models, or (iii) perform diffusion inversion to extract attention cues from pre-trained image diffusion models-thereby treating segmentation as an auxiliary process. In this paper, we propose GS (Generative Segmentation), a novel framework that formulates segmentation itself as a generative task via label diffusion. Instead of generating images conditioned on label maps and text, GS reverses the generative process: it directly generates segmentation masks from noise, conditioned on both the input image and the accompanying language description. This paradigm makes label generation the primary modeling target, enabling end-to-end training with explicit control over spatial and semantic fidelity. To demonstrate the effectiveness of our approach, we evaluate GS on Panoptic Narrative Grounding (PNG), a representative and challenging benchmark for multimodal segmentation that requires panoptic-level reasoning guided by narrative captions. Experimental results show that GS significantly outperforms existing discriminative and diffusion-based methods, setting a new state-of-the-art for language-driven segmentation.
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