Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and methods for classic RES task heavily rely on the prior assumption that one expression must refer to object-level targets. In this paper, we take a step further to finer-grained part-level RES task. To promote the object-level RES task towards finer-grained vision-language understanding, we put forward a new multi-granularity referring expression segmentation (MRES) task and construct an evaluation benchmark called RefCOCOm by manual annotations. By employing our automatic model-assisted data engine, we build the largest visual grounding dataset namely MRES-32M, which comprises over 32.2M high-quality masks and captions on the provided 1M images. Besides, a simple yet strong model named UniRES is designed to accomplish the unified object-level and part-level grounding task. Extensive experiments on our RefCOCOm for MRES and three datasets (i.e., RefCOCO(+/g) for classic RES task demonstrate the superiority of our method over previous state-of-the-art methods. To foster future research into fine-grained visual grounding, our benchmark RefCOCOm, the MRES-32M dataset and model UniRES will be publicly available at https://github.com/Rubics-Xuan/MRES

翻译：指代表达式分割旨在分割与描述性自然语言表达式匹配的实体的前景掩码。以往经典指代表达式分割任务的数据集和方法严重依赖于一个先验假设：一个表达式必须指代物体级目标。在本文中，我们进一步迈向更细粒度的部件级指代表达式分割任务。为了将物体级指代表达式任务推向更细粒度的视觉-语言理解，我们提出了一个新的多粒度指代表达式分割任务，并通过人工标注构建了一个名为RefCOCOm的评估基准。通过采用我们自动化的模型辅助数据引擎，我们构建了迄今最大的视觉定位数据集，即MRES-32M，该数据集在所提供的100万张图像上包含超过3220万个高质量掩码和标题。此外，我们设计了一个简单而强大的模型，名为UniRES，以完成统一的物体级和部件级定位任务。在我们的RefCOCOm基准（用于MRES任务）以及三个数据集（即RefCOCO(+/g)用于经典指代表达式分割任务）上的大量实验表明，我们的方法优于先前的最先进方法。为促进未来细粒度视觉定位研究，我们的基准RefCOCOm、MRES-32M数据集和UniRES模型将在https://github.com/Rubics-Xuan/MRES 公开提供。