ChartREG++：面向多样化指代线索和多目标指代的图表指代表达式定位基准与改进
ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
AI总结：
针对现有图表指代表达式定位基准的局限，提出支持多种定位形式、多目标指代、多样化线索和图表类型的基准，并利用代码驱动合成流水线生成像素级实例掩码，训练实例分割模型集成到多模态定位框架，显著提升性能。
摘要：
指代表达式定位是视觉定位的核心问题，广泛用于视觉与语言模型的空间定位与推理诊断，但以往工作多聚焦于自然图像。相比之下，现有的图表指代表达式定位基准存在局限：(1) 大多采用边界框，限制了精细图表元素的定位精度；(2) 大多假设单个或两个指代目标实例，无法处理多实例目标指代；(3) 语言表达过度依赖文本线索或数据排名线索；(4) 仅覆盖狭窄的图表类型范围。为解决这些问题，我们引入了一个图表指代表达式定位基准，系统性地支持多种定位形式、多个指代目标、多样化定位线索和多种图表类型。在代表性多模态大模型上的结果揭示了显著的性能差距。我们进一步引入了一个代码驱动的合成流水线，利用绘图程序与渲染图表基元之间的固有对齐，跨图表元素类型和粒度生成像素级精确的实例掩码。我们使用合成掩码训练了一个实例分割模型，并将其集成到一个通用的多模态定位框架中。最终系统在我们的基准上持续优于基线，并很好地泛化到从ChartQA导出的真实图表定位基准。
英文摘要：
Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision and language models, yet most prior work focuses on natural images. In contrast, existing chart referring expression grounding-related benchmarks remain limited: (1) they largely adopt bounding boxes, constraining localization precision for fine chart elements (2) they mostly assume a single and two referred target instances, failing to handle multi-instance target references; (3) the language expressions over-rely on textual cues or data-rank clues (4) they cover only a narrow range of chart types. To address these issues, we introduce a chart referring expression grounding benchmark that systematically supports multiple localization forms, multiple referred targets, diverse grounding cues and diverse chart types. Results across representative multimodal large models reveal a significant performance gap. We further introduce a code-driven synthesis pipeline that exploits the inherent alignment between plotting programs and rendered chart primitives to derive pixel accurate instance masks across chart element types and granularities. We train an instance segmentation model with the synthesized masks and integrate it into a general-purpose multimodal grounding framework. The resulting system consistently outperforms baselines on our benchmark and generalizes well to a ChartQA-derived real-chart grounding benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
