ChartREG++: New Benchmark and Methods for Improving Chart Understanding in AI Models
Researchers introduced ChartREG++, a new benchmark for testing how well vision-language AI models can locate and identify specific elements within charts based on text descriptions. The benchmark addresses limitations in existing datasets by supporting multiple target references, diverse chart types, and varied language cues rather than relying solely on text or data rankings. This work is significant because chart understanding is crucial for AI systems to effectively process visual data in documents and reports.
A team of researchers has developed ChartREG++, a comprehensive benchmark designed to evaluate and improve how multimodal AI models ground referring expressions in charts—that is, how well they can locate specific chart elements when given text descriptions. The new benchmark overcomes four key limitations in prior work: it supports multiple localization formats beyond simple bounding boxes, handles references to multiple target instances simultaneously, incorporates diverse types of grounding cues beyond text and data rankings, and covers a wider range of chart types. The researchers also introduced a code-driven synthesis pipeline that generates pixel-accurate instance masks by leveraging the alignment between plotting code and rendered chart elements. Testing on representative multimodal large models revealed significant performance gaps, and their proposed instance segmentation approach consistently outperformed baselines while generalizing well to real-world chart data derived from ChartQA benchmarks.
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- arXiv cs.AICenter
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
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