ChartAB: A Benchmark for Chart Grounding & Dense Alignment
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.
69045abf2c556835fa67f163
Aniruddh Bansal
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2025-11-04T20:27:20.307Z
6622e7df90f8f88942187d99
/avatars/49f514d4cedecfee2bf37b80d14a13c0.svg
Aniruddh Bansal
aniruddhbansal2000
user
aniruddhbansal2000
69045abf2c556835fa67f164
Davit Soselia
69045abf2c556835fa67f165
Dang Nguyen
69045abf2c556835fa67f166
Tianyi Zhou
claimed_verified
2025-10-31T15:23:15.229Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
zhoutianyi
user
zhoutianyi
ChartAB: A Benchmark for Chart Grounding & Dense Alignment
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.
A new benchmark, ChartAlign Benchmark (ChartAB), evaluates vision-language models' performance in chart grounding, including data extraction, element localization, and attribute recognition, revealing their strengths and weaknesses.
vision-language models chart grounding tabular data visualization elements attributes JSON template two-stage inference perception biases weaknesses robustness hallucinations