Search Results for author: Lakshmi Narasimhan Govindarajan

Found 5 papers, 1 papers with code

Stable and expressive recurrent vision models

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.

Panoptic Segmentation

Robust pose tracking with a joint model of appearance and shape

no code implementations28 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.

Pose Tracking

An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

no code implementations4 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.

Activity Recognition

Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups

no code implementations13 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.

Action Recognition Pose Estimation

Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform

no code implementations7 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.

Action Detection Action Recognition

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