Despite significant advancements in Large Vision-Language Models (LVLMs), existing pixel-grounding models operate on single-image settings, limiting their ability to perform detailed, fine-grained comparisons across multiple images. Conversely, current multi-image understanding models lack pixel-level grounding. Our work addresses this gap by introducing the task of multi-image pixel-grounded reasoning segmentation, and PRIMA, a novel LVLM that integrates pixel-level grounding with robust multi-image reasoning capabilities to produce contextually rich, pixel-grounded explanations. Central to PRIMA is an efficient vision module that queries fine-grained visual representations across multiple images, reducing TFLOPs by $25.3\%$. To support training and evaluation, we curate $M^4Seg$, a new reasoning segmentation benchmark consisting of $\sim$224K question-answer pairs that require fine-grained visual understanding across multiple images. Experimental results demonstrate PRIMA outperforms state-of-the-art baselines.

翻译：尽管大型视觉-语言模型（LVLMs）已取得显著进展，但现有的像素级定位模型仅能处理单图像场景，限制了其跨多幅图像进行细致、细粒度比较的能力。反之，当前的多图像理解模型缺乏像素级定位功能。本研究通过引入多图像像素级定位推理分割任务以及PRIMA模型来填补这一空白——PRIMA是一种新型LVLM，它将像素级定位与强大的多图像推理能力相结合，从而生成上下文丰富且基于像素定位的解释。PRIMA的核心是一个高效的视觉模块，该模块可跨多幅图像查询细粒度视觉表征，并将TFLOPs降低$25.3\%$。为支持训练与评估，我们构建了$M^4Seg$——一个包含$\sim$224K个问答对的新型推理分割基准数据集，该数据集要求模型具备跨多图像的细粒度视觉理解能力。实验结果表明，PRIMA在性能上超越了现有最先进的基线模型。