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Faulty negatives and unreliable positives can be effectively managed in multi-modal learning, leading to significant improvements in driver distraction detection accuracy.
Correcting errors in long-video understanding doesn't have to be a nightmare: IMPACT-CYCLE slashes human arbitration costs by 4.8x while boosting VQA accuracy by intelligently decomposing the task and focusing human effort where it matters most.
