no code implementations • 7 Jun 2016 • Chi Xu, Lakshmi Narasimhan Govindarajan, Li Cheng
Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion.
no code implementations • 13 Sep 2016 • Chi Xu, Lakshmi Narasimhan Govindarajan, Yu Zhang, Li Cheng
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
no code implementations • 4 Jan 2017 • Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan, Shu Wang, Bin Hu, Li Cheng, David S. Rosenblum
We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.
no code implementations • 28 Jun 2018 • Yuliang Guo, Lakshmi Narasimhan Govindarajan, Benjamin Kimia, Thomas Serre
We present a novel approach for estimating the 2D pose of an articulated object with an application to automated video analysis of small laboratory animals.
1 code implementation • NeurIPS 2020 • Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex Liu, Thomas Serre
We posit that the effectiveness of recurrent vision models is bottlenecked by the standard algorithm used for training them, "back-propagation through time" (BPTT), which has O(N) memory-complexity for training an N step model.
no code implementations • 22 Sep 2023 • Lakshmi Narasimhan Govindarajan, Rex G Liu, Drew Linsley, Alekh Karkada Ashok, Max Reuter, Michael J Frank, Thomas Serre
Humans learn by interacting with their environments and perceiving the outcomes of their actions.