标题： R2SM：指代与推理用于选择性掩码 显示英文标题

标题： R2SM: Referring and Reasoning for Selective Masks

摘要： 我们引入了一个新任务，称为指引用例和推理选择性蒙版（R2SM），它通过结合由用户意图驱动的蒙版类型选择扩展了文本引导的分割。 该任务挑战视觉语言模型，要求它们仅根据自然语言提示来确定是生成模态（可见）还是非模态（完整）分割蒙版。 为了支持R2SM任务，我们提出了R2SM数据集，该数据集通过增强COCOA-cls、D2SA和MUVA的注释构建而成。 R2SM数据集包括模态和非模态文本查询，每个查询都与相应的地面实况蒙版配对，使模型能够针对按用户意图分割图像的能力进行微调和评估。 具体而言，该任务要求模型解释给定提示是否仅指的是对象的可见部分，还是包括被遮挡区域在内的完整形状，然后生成适当的分割。 例如，如果提示明确请求部分隐藏对象的完整形状，则模型应输出一个非模态掩码以完成被遮挡的部分。 相比之下，没有明确提到隐藏区域的提示应该生成标准的模态掩码。 R2SM基准为推进多模态推理和意图感知分割的研究提供了具有挑战性和洞察力的测试平台。 显示英文摘要

摘要： We introduce a new task, Referring and Reasoning for Selective Masks (R2SM), which extends text-guided segmentation by incorporating mask-type selection driven by user intent. This task challenges vision-language models to determine whether to generate a modal (visible) or amodal (complete) segmentation mask based solely on natural language prompts. To support the R2SM task, we present the R2SM dataset, constructed by augmenting annotations of COCOA-cls, D2SA, and MUVA. The R2SM dataset consists of both modal and amodal text queries, each paired with the corresponding ground-truth mask, enabling model finetuning and evaluation for the ability to segment images as per user intent. Specifically, the task requires the model to interpret whether a given prompt refers to only the visible part of an object or to its complete shape, including occluded regions, and then produce the appropriate segmentation. For example, if a prompt explicitly requests the whole shape of a partially hidden object, the model is expected to output an amodal mask that completes the occluded parts. In contrast, prompts without explicit mention of hidden regions should generate standard modal masks. The R2SM benchmark provides a challenging and insightful testbed for advancing research in multimodal reasoning and intent-aware segmentation.