ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
arXiv:2605.07415v2 Announce Type: replace-cross Abstract: Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision and language models, yet most prior work focuses on natural images. In contrast, existing chart referring expression grounding-related benchmarks remain limited: (1) they largely adopt bounding boxes, constraining localization precision for fine chart elements (2) they mostly assume a single and two referred target instances, failing to handle multi-instance target references; (3) the langu
The proliferation of visual data, including charts, necessitates more robust AI models for complex visual understanding and interaction, especially as AI pushes towards multimodal reasoning and specialized application domains.
Improving chart understanding capabilities is crucial for advancing AI's ability to interpret structured visual information, which is pervasive in business, scientific research, and data-driven decision-making.
This work introduces a new benchmark and methodology that addresses current limitations in chart referring expression grounding, paving the way for more precise and versatile AI interpretations of complex charts.
- · AI researchers
- · Data scientists
- · Business intelligence platforms
- · Visual analytics software
- · Manual data interpretation processes
- · AI models with limited visual reasoning capabilities
More accurate and nuanced AI analysis of charts and graphs becomes feasible.
This leads to improved automated reporting, data extraction, and decision support systems across various industries.
The development of highly specialized AI agents capable of autonomously generating insights from complex visual data could accelerate scientific discovery and economic analysis.
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Read at arXiv cs.CL