no code implementations • CVPR 2017 • Ajjen Joshi, Soumya Ghosh, Margrit Betke, Stan Sclaroff, Hanspeter Pfister
Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy.
no code implementations • 12 Feb 2020 • Nataniel Ruiz, Hao Yu, Danielle A. Allessio, Mona Jalal, Ajjen Joshi, Thomas Murray, John J. Magee, Jacob R. Whitehill, Vitaly Ablavsky, Ivon Arroyo, Beverly P. Woolf, Stan Sclaroff, Margrit Betke
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
1 code implementation • 4 Oct 2020 • Sandipan Banerjee, Ajjen Joshi, Prashant Mahajan, Sneha Bhattacharya, Survi Kyal, Taniya Mishra
Building facial analysis systems that generalize to extreme variations in lighting and facial expressions is a challenging problem that can potentially be alleviated using natural-looking synthetic data.
no code implementations • 21 Oct 2020 • Ajjen Joshi, Survi Kyal, Sandipan Banerjee, Taniya Mishra
However, developing drowsiness detection systems that work well in real-world scenarios is challenging because of the difficulties associated with collecting high-volume realistic drowsy data and modeling the complex temporal dynamics of evolving drowsy states.
no code implementations • 5 Dec 2020 • Sandipan Banerjee, Ajjen Joshi, Jay Turcot, Bryan Reimer, Taniya Mishra
Distracted drivers are dangerous drivers.