Chart Understanding
14 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Chart Understanding
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.
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.
StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding
Charts are common in literature across different scientific fields, conveying rich information easily accessible to readers.
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.
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
Charts are important for presenting and explaining complex data relationships.
