Chart Understanding
27 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Chart Understanding
SBS Figures
Most implemented papers
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
fuxiaoliu/mmc
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Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
unimodal4reasoning/chartvlm
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Specifically, StructChart first reformulates the chart data from the tubular form (linearized CSV) to STR, which can friendlily reduce the task gap between chart perception and reasoning.
OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics
To address this limitation, we introduce OrionBench, a benchmark designed to support the development of accurate object detection models for charts and HROs in infographics.
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
We showcase the utility of this dataset through: 1) improving infographic chart understanding via fine-tuning, 2) benchmarking code generation for infographic charts, and 3) enabling example-based infographic chart generation.
InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
However, existing visual-question answering benchmarks fall short in evaluating these capabilities of MLLMs due to the lack of paired plain charts and visual-element-based questions.
DVQA: Understanding Data Visualizations via Question Answering
Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them.
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules
zhiqic/chartreader
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ICCV 2023
We evaluate ChartReader on Chart-to-Table, ChartQA, and Chart-to-Text tasks, demonstrating its superiority over existing methods.
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
vis-nlp/unichart
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Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data.
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Accordingly, we propose Vary, an efficient and effective method to scale up the vision vocabulary of LVLMs.
Improving Language Understanding from Screenshots
An emerging family of language models (LMs), capable of processing both text and images within a single visual view, has the promise to unlock complex tasks such as chart understanding and UI navigation.
