ChartAB: A Benchmark for Chart Grounding & Dense Alignment

• URL: http://arxiv.org/abs/2510.26781v2
• Date: Mon, 03 Nov 2025 06:01:32 GMT
• Title: ChartAB: A Benchmark for Chart Grounding & Dense Alignment
• Authors: Aniruddh Bansal, Davit Soselia, Dang Nguyen, Tianyi Zhou,
• Abstract summary: We introduce a novel "ChartAlign Benchmark (ChartAB)" to provide a comprehensive evaluation of vision-language models (VLMs)<n>By incorporating a novel two-stage inference workflow, the benchmark can further evaluate VLMs capability to align and compare elements/attributes across two charts.<n>Our analysis of evaluations reveals new insights into their perception biases, weaknesses, robustness, and hallucinations in chart understanding.
• Score: 17.16234793106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• ChartAnchor: Chart Grounding with Structural-Semantic Fidelity [19.798612765001746]
  Chart grounding refers to the bidirectional alignment between a chart's visual appearance and the structured semantics.<n>ChartAnchor is a benchmark of 8k+ chart-table-code triples spanning 30 chart types drawn from diverse real-world and augmented sources.<n>A multi-level evaluation framework integrates semantic validation, stylistic analysis, and perceptual metrics to assess both structural and content-level correctness.
  arXiv  Detail & Related papers  (2025-11-30T18:28:09Z)
• ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning [54.86473583610112]
  We propose PointCoT, which integrates reflective interaction into chain-of-thought reasoning in charts.<n>By prompting MLLMs to generate bounding boxes and re-render charts based on location annotations, we establish connections between textual reasoning steps and visual grounding regions.<n>We develop two instruction-tuned models, ChartPointQ2 and ChartPointQ2.5, which outperform state-of-the-art across several chart benchmarks.
  arXiv  Detail & Related papers  (2025-11-29T04:01:55Z)
• ChartLens: Fine-grained Visual Attribution in Charts [106.44872805609673]
  Post-Hoc Visual Attribution for Charts identifies fine-grained chart elements that validate a given chart-associated response.<n>We propose ChartLens, a novel chart attribution algorithm that uses segmentation-based techniques to identify chart objects.<n>Our evaluations show that ChartLens improves fine-grained attributions by 26-66%.
  arXiv  Detail & Related papers  (2025-05-25T23:17:32Z)
• RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning [63.599057862999]
  RefChartQA is a novel benchmark that integrates Chart Question Answering (ChartQA) with visual grounding.<n>Our experiments demonstrate that incorporating spatial awareness via grounding improves response accuracy by over 15%.
  arXiv  Detail & Related papers  (2025-03-29T15:50:08Z)
• On Pre-training of Multimodal Language Models Customized for Chart Understanding [83.99377088129282]
  This paper explores the training processes necessary to improve MLLMs' comprehension of charts.<n>We introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension.
  arXiv  Detail & Related papers  (2024-07-19T17:58:36Z)
• ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning [55.22996841790139]
  We benchmark the ability of off-the-shelf Multi-modal Large Language Models (MLLMs) in the chart domain.<n>We construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data.<n>We develop ChartVLM to offer a new perspective on handling multi-modal tasks that strongly depend on interpretable patterns.
  arXiv  Detail & Related papers  (2024-02-19T14:48:23Z)
• StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding [54.45681512355684]
  Current chart-related tasks focus on either chart perception that extracts information from the visual charts, or chart reasoning given the extracted data.<n>We introduce StructChart, a novel framework that leverages Structured Triplet Representations (STR) to achieve a unified and label-efficient approach.
  arXiv  Detail & Related papers  (2023-09-20T12:51:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.