2 citations · 3 across the 4 of their papers we have counts for
4 papers
Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection
David J. Lerch, Livien Majer, Zeyun Zhong +3
Robust self-supervised learning of multi-modal video representations is critical for real-world applications such as driver distraction detection, where multiple sensors provide co…
Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset
David J. Lerch, Sarath Mulugurthi, Manuel Martin +2
Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on general datasets and struggle…
DiffAnt: Diffusion Models for Action Anticipation
Zeyun Zhong, Chengzhi Wu, Manuel Martin +3
Anticipating future actions is inherently uncertain. Given an observed video segment containing ongoing actions, multiple subsequent actions can plausibly follow. This uncertainty…
A Survey on Deep Learning Techniques for Action Anticipation
Zeyun Zhong, Manuel Martin, Michael Voit +2
The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numer…