Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection 事件

Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection arXiv:2606.02352v1 Announce Type: new Abstract: Robust self-supervised learning of multi-modal video representations is critical for real-world applications such as driver distraction detection, where multiple sensors provide complementary but noisy signals. Conventional contrastive objectives, such as InfoNCE, assume all negatives are equally informative and all positives are reliable. However, t