MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
Open-sourced data ecosystem in autonomous driving: the present and future,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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CogDriver-Agent with sparse temporal memory and spatiotemporal distillation on CogDriver-Data achieves 22% higher closed-loop Driving Score on Bench2Drive and 21% lower mean L2 error on nuScenes.
The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.
citing papers explorer
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MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
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CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving
CogDriver-Agent with sparse temporal memory and spatiotemporal distillation on CogDriver-Data achieves 22% higher closed-loop Driving Score on Bench2Drive and 21% lower mean L2 error on nuScenes.
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Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.