m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning
Learn to evaluate vision-language models with m2sv, a scalable benchmark for map-to-street-view spatial reasoning, to improve their performance on aligning abstract overhead representations with egocentric views
- Download the m2sv dataset from the arXiv repository
- Preprocess the map and Street View image data for use in the benchmark
- Implement a vision-language model to participate in the m2sv challenge
- Evaluate the model's performance using the m2sv metric
- Compare the results with other state-of-the-art models and analyze the findings
Computer vision engineers and researchers can use m2sv to test and improve the spatial reasoning capabilities of their vision-language models, while data scientists can utilize it to benchmark and compare the performance of different models
💡 m2sv provides a comprehensive evaluation framework for vision-language models to improve their spatial reasoning capabilities
📍 Introducing m2sv: a scalable benchmark for map-to-street-view spatial reasoning to evaluate vision-language models 🚀
Key Takeaways
Learn to evaluate vision-language models with m2sv, a scalable benchmark for map-to-street-view spatial reasoning, to improve their performance on aligning abstract overhead representations with egocentric views
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Abstract:
arXiv:2601.19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views. We introduce m2sv, a scalable benchmark for map-to-street-view spatial reasoning that asks models to infer camera viewing direction by aligning a north-up overhead map with a Street View image captured at the same real-world inte
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