Deep Learning Conf 2016

A conference on deep learning.

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

Submitted by Arjun Jain (@stencilman) on Wednesday, 11 May 2016

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Technical level

Advanced

Section

Crisp talk

Status

Confirmed & Scheduled

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Abstract

We propose a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques.

Outline

http://arxiv.org/pdf/1406.2984v2.pdf

Requirements

Background knowledge of ConvNets and Markov Random Fields

Speaker bio

Arjun Jain is the cofounder of Perceptive Code. Prior to this, he was researcher with a special project team at Apple and a post-doctoral researcher at the Computer Science department at New York University’s Courant Institute. He received his Ph.D. in Computer Science from the Max-Planck Institute for Informatics in Germany. Broadly, his research lies at the interface of computer graphics, computer vision, and machine learning, with a focus on human pose estimation and data-driven artistic content creation tools. Arjun has worked as a developer for several companies, including Yahoo! in Bangalore and Weta Digital in New Zealand. Arjun served as a developer for Weta Digital’s vision-based motion capture system. This system has been used in many feature films, and Arjun was credited for his work in Steven Spielberg’s, The Adventures of Tintin. Arjun’s work has resulted in several academic publications, a patent, and has been featured by mainstream media, including in the magazines: New Scientist, Discovery, BCC, Vogue, Wired, India Today, and The Hollywood Reporter, among other outlets.

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Comments

  • 1
    eyebies (@codemonkey) 2 years ago

    Looking forward to your talk!

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