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Fine-tuning vision-language models on a richly described dataset boosts their accuracy in interpreting subtle driver actions by 10 points, revealing a critical gap in existing training methods.
Faulty negatives and unreliable positives can be effectively managed in multi-modal learning, leading to significant improvements in driver distraction detection accuracy.
