Charts play an important role in visualization, reasoning, data analysis, and the exchange of ideas among humans. However, existing vision-language models (VLMs) still lack accurate perception of details and struggle to extract fine-grained structures from charts. Such limitations in chart grounding also hinder their ability to compare multiple charts and reason over them. In this paper, we introduce a novel "ChartAlign Benchmark (ChartAB)" to provide a comprehensive evaluation of VLMs in chart grounding tasks, i.e., extracting tabular data, localizing visualization elements, and recognizing various attributes from charts of diverse types and complexities. We design a JSON template to facilitate the calculation of evaluation metrics specifically tailored for each grounding task. By incorporating a novel two-stage inference workflow, the benchmark can further evaluate VLMs capability to align and compare elements/attributes across two charts. Our analysis of evaluations on several recent VLMs reveals new insights into their perception biases, weaknesses, robustness, and hallucinations in chart understanding. These findings highlight the fine-grained discrepancies among VLMs in chart understanding tasks and point to specific skills that need to be strengthened in current models.
VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought
Department of Computer Science and Artificial Intelligence, Dongguk University（东国大学计算机科学与人工智能系）；Department of Electronics and Electrical Engineering, Dongguk University（东国大学电子与电气工程系）
ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Key Laboratory of Data Space Technology and System（数据空间技术与系统国家重点实验室）；School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）
VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（香港科技大学新兴跨学科领域分部）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
ENC-Bench: A Benchmark for Evaluating Multimodal Large Language Models in Electronic Navigational Chart Understanding
National University of Defense Technology（国防科技大学）；Intelligent Game and Decision Lab（智能游戏与决策实验室）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
DOGR: Towards Versatile Visual Document Grounding and Referring
Xi’an Jiaotong University（西安交通大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；City University of Hongkong（香港城市大学）；Institute of Automation, CAS（中国科学院自动化研究所）；Harvard University（哈佛大学）；vivo Mobile Communication Co.（vivo移动通信公司）
