OmniMapBench: Benchmarking Visual-Centric Reasoning on Diverse Map Documents
OmniMapBench uses VDI to validate visual reasoning on maps; top LVLM achieves only 75.03% accuracy.
Key Findings
Methodology
OmniMapBench includes 1,603 maps and 2,096 QA pairs across 9 categories, created via manual annotation and cross-validation. Introduced the Visual Dependency Index (VDI) to quantify reliance on visual information and evaluated 25 LVLMs.
Key Results
- Top-performing LVLM achieved only 75.03% accuracy on OmniMapBench, highlighting significant challenges in complex visual reasoning.
- OmniMapBench exhibits higher VDI than other benchmarks (e.g., DocVQA), confirming its focus on irreducible visual reasoning.
- Performance varies significantly across tasks, with multi-step reasoning being particularly challenging for current models.
Significance
OmniMapBench addresses the limitations of text-reducible benchmarks, advancing LVLM research in visual reasoning. Its diverse map types and high VDI validate its real-world relevance and difficulty.
Technical Contribution
Introduced the VDI metric to quantify visual dependency; developed a high-quality dataset spanning 9 map categories; conducted a comprehensive evaluation of 25 LVLMs, identifying performance gaps.
Novelty
OmniMapBench is the first benchmark to focus on irreducible visual reasoning for map documents, with VDI filling a critical gap in quantifying visual dependency.
Limitations
- Models struggle with multi-step reasoning, especially for complex spatial relationships.
- Dataset size is relatively small, potentially limiting generalization.
- Certain map types (e.g., game maps) may not generalize to all real-world scenarios.
Future Work
Future work includes scaling the dataset, adding multilingual support, and developing more efficient visual reasoning algorithms to improve multi-step reasoning.
AI Executive Summary
OmniMapBench is a novel benchmark designed to evaluate large vision-language models (LVLMs) on visual reasoning tasks involving map documents. Existing benchmarks often rely heavily on text-based shortcuts, neglecting the importance of visual information. OmniMapBench introduces the Visual Dependency Index (VDI) to quantify a task's reliance on visual data, significantly raising the bar for evaluation.
The dataset includes 1,603 map images and 2,096 question-answer pairs across 9 categories, ranging from transit maps to fictional game maps. Experiments reveal that even the best-performing LVLM achieves only 75.03% accuracy, underscoring the challenges of complex visual reasoning tasks.
OmniMapBench is a significant step forward in advancing visual reasoning research, providing a more realistic and challenging testbed for LVLMs. Future work will focus on expanding the dataset, supporting multilingual evaluation, and enhancing model capabilities for multi-step reasoning tasks.
Deep Analysis
Background
Recent advances in LVLMs have highlighted the need for benchmarks that evaluate genuine visual reasoning. Existing benchmarks like DocVQA and ChartQA often rely on text-reducible tasks, failing to test models on irreducible visual information. Maps, with their spatial and symbolic complexity, offer an ideal testbed.
Core Problem
Current benchmarks inadequately assess LVLMs' visual reasoning, especially in tasks requiring multi-step reasoning and complex spatial relationships.
Innovation
OmniMapBench introduces VDI to measure visual dependency and provides irreducible visual reasoning tasks. Its dataset spans 9 diverse map categories, ensuring high visual and task diversity, and is rigorously annotated and validated.
Methodology
- �� Data Collection: Gathered 5,000 maps via web scraping and manual sourcing, filtered to 2,000 high-quality images.
- �� Annotation: Created 3,386 QA pairs manually, reduced to 2,096 through cross-validation.
- �� VDI Metric: Quantifies performance drop when images are replaced with text descriptions.
Experiments
Evaluated 25 LVLMs, including open-source and proprietary models. Assessed performance using VDI and accuracy across tasks like single-step and multi-step reasoning.
Results
OmniMapBench's VDI is significantly higher than other benchmarks, confirming its focus on visual reasoning. The top model achieved only 75.03% accuracy, highlighting current limitations.
Applications
Useful for evaluating LVLMs in map navigation, remote sensing, and other visual reasoning tasks, with potential industrial applications.
Limitations & Outlook
Dataset size and diversity are limited, and some tasks demand high computational resources. Future work should focus on scaling the dataset and improving model efficiency.
Plain Language Accessible to non-experts
Imagine you're navigating a new city using a map. Traditional benchmarks are like giving you written directions, while OmniMapBench asks you to interpret the map directly. This approach better mirrors real-world scenarios.
ELI14 Explained like you're 14
Think of playing a game where you have to find treasure on a map. OmniMapBench is like asking AI, 'Which mark is the treasure?' It can't just rely on text hints—it has to look at the map and understand it!
Glossary
Visual Dependency Index (VDI)
A metric quantifying how much a task relies on visual information.
Used to measure OmniMapBench's visual reasoning difficulty.
Large Vision-Language Models (LVLMs)
AI models combining vision and language understanding.
Evaluated on OmniMapBench for reasoning capabilities.
Multi-step reasoning
Tasks requiring multiple logical steps to reach an answer.
A key challenge in OmniMapBench.
Irreducible visual information
Visual content that cannot be fully replaced by text descriptions.
Core to OmniMapBench's tasks.
DocVQA
A document-based visual question-answering benchmark.
Compared against OmniMapBench in experiments.
Open Questions Unanswered questions from this research
- 1 How can LVLMs improve multi-step reasoning performance?
- 2 What methods can scale datasets to enhance generalization?
Applications
Immediate Applications
Map Navigation
Evaluates AI's ability to navigate complex maps like transit or mall layouts.
Remote Sensing Analysis
Analyzes satellite imagery for terrain and environmental changes.
Long-term Vision
Smart City Planning
Uses AI to optimize urban layouts and traffic design through map analysis.
Abstract
Recent advancements in LVLMs necessitate robust benchmarks for complex, visually grounded reasoning. A critical limitation is identified in many document understanding benchmarks: visual content is often reducible to text, enabling high performance without genuine visual grounding. To address this limitation, OmniMapBench is introduced to foster visual-centric reasoning for map documents. The benchmark comprises 2,096 manually annotated question-answer pairs across 1,603 map documents from nine categories. It is designed to probe a hierarchy of skills, ranging from perception to multi-step visual reasoning. To quantify benchmark properties, a simple yet effective benchmark-level metric is proposed: the Visual Dependency Index (VDI), defined as the accuracy drop when images are replaced with question-agnostic descriptions. OmniMapBench exhibits higher VDI than established benchmarks, which quantitatively validates its focus on irreducible visual reasoning. Comprehensive evaluations of 25 leading LVLMs are conducted on OmniMapBench. A significant performance gap is observed, with the top-performing model achieving only 75.03\% accuracy. This result underscores the challenges posed by OmniMapBench to current LVLMs. This work aims to catalyze progress in visual-centric reasoning for document understanding of LVLMs. The dataset and code are publicly available at https://github.com/SIGMME/OmniMapBench.