no code implementations • ICCV 2023 • David Fan, Jue Wang, Shuai Liao, Yi Zhu, Vimal Bhat, Hector Santos-Villalobos, Rohith MV, Xinyu Li
This suggests that the random masking strategy that is inherited from the image MAE is less effective for video MAE.
no code implementations • ICCV 2023 • Najmeh Sadoughi, Xinyu Li, Avijit Vajpayee, David Fan, Bing Shuai, Hector Santos-Villalobos, Vimal Bhat, Rohith MV
Previous research has studied the task of segmenting cinematic videos into scenes and into narrative acts.
no code implementations • 13 Mar 2023 • David Fan, Deyu Yang, Xinyu Li, Vimal Bhat, Rohith MV
Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain.
no code implementations • 15 Feb 2022 • Cristian-Paul Bara, Qing Ping, Abhinav Mathur, Govind Thattai, Rohith MV, Gaurav S. Sukhatme
We introduce a novel privacy-preserving methodology for performing Visual Question Answering on the edge.
no code implementations • CVPR 2019 • Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Rohith MV, Stefan Stojanov, James M. Rehg
Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data.
Ranked #73 on 3D Human Pose Estimation on MPI-INF-3DHP (AUC metric)
no code implementations • 22 Aug 2018 • Dylan Drover, Rohith MV, Ching-Hang Chen, Amit Agrawal, Ambrish Tyagi, Cong Phuoc Huynh
We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks.