A. Saxena

Publications
Influence

Deep learning for detecting robotic grasps

I. Lenz, H. Lee, A. Saxena
Computer Science
Int. J. Robotics Res.
15 January 2013

We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. Expand

Structural-RNN: Deep Learning on Spatio-Temporal Graphs

Ashesh Jain, A. Zamir, S. Savarese, A. Saxena
Computer Science
IEEE Conference on Computer Vision and Pattern…
17 November 2015

We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. Expand

Learning human activities and object affordances from RGB-D videos

H. Koppula, Rudhir Gupta, A. Saxena
Computer Science
Int. J. Robotics Res.
3 October 2012

We consider the problem of extracting a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, of their interactions with the objects in the form of associated affordances. Expand

Unstructured human activity detection from RGBD images

Jaeyong Sung, C. Ponce, B. Selman, A. Saxena
Computer Science
IEEE International Conference on Robotics and…
1 July 2011

We perform detection and recognition of human activity in unstructured environments using a hierarchical maximum entropy Markov model based on a two-layered graph structure. Expand

Learning Depth from Single Monocular Images

A. Saxena, Sung H. Chung, A. Ng
Computer Science, Mathematics
NIPS
5 December 2005

We consider the task of depth estimation from a single monocular image. Expand

A dynamic ID-based remote user authentication scheme

M. Das, A. Saxena, V. Gulati
Computer Science
IEEE Transactions on Consumer Electronics
1 May 2004

We present a dynamic ID-based remote user authentication scheme using smart cards, which allows the users to choose and change their passwords freely. Expand

Human Activity Detection from RGBD Images

Jaeyong Sung, C. Ponce, B. Selman, A. Saxena
Computer Science
Plan, Activity, and Intent Recognition
1 July 2011

We perform activity recognition using an inexpensive RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Expand

Robotic Grasping of Novel Objects using Vision

A. Saxena, Justin Driemeyer, A. Ng
Computer Science
Int. J. Robotics Res.
1 February 2008

We consider the problem of grasping novel objects, specifically objects that are being seen for the first time through vision. Expand

3-D Depth Reconstruction from a Single Still Image

A. Saxena, Sung H. Chung, A. Ng
Computer Science
International Journal of Computer Vision
2007

We apply a supervised learning approach to the problem of 3-d depth estimation from a single image. Expand

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Ashesh Jain, H. Koppula, B. Raghavan, Shane Soh, A. Saxena
Computer Science
IEEE International Conference on Computer Vision…
10 April 2015

We propose an Autoregressive Input-Output Hidden Markov Model (AIO-HMM) to model the contextual information alongwith the maneuvers. Expand

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