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

Jul 2016

27 Mon

28 Tue

29 Wed

30 Thu

1 Fri 08:45 AM – 06:15 PM IST

2 Sat 08:15 AM – 02:15 PM IST

3 Sun

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CMRIT College

Deep Learning is a new area of research that is getting us closer in achieving one of the primary objectives of Machine Learning – Artificial Intelligence.
It is used widely in the fields of Image Recognition, Natural Language Processing (NLP) and Video Classification.

Format #

Deep Learning Conf is a single day conference followed by workshops on the second day. The conference will have full, crisp and lightning talks from morning to evening. The workshops on the next day will introduce participants to neural networks followed by two tracks of three-hour workshops on NLP and Computer Vision / AI. Participants can join either one of the two workshop tracks.

##Tracks
We are looking for talks and workshops from academics and practitioners of Deep Learning on the following topics:

Applications of Deep Learning in software.
Applications of Deep Learning in hardware.
Conceptual talks and cutting edge research on Deep Learning.
Building businesses with Deep Learning at the core.

We are inviting proposals for:

Full-length 40 minute talks.
Crisp 15-minute talks.
Lightning talks of 5 mins duration.

Selection process #

Proposals will be filtered and shortlisted by an Editorial Panel. Along with your proposal, you must share the following details:

Links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience.
Blog posts you may have written related to your proposal.
Outline of your proposed talk – either in the form of a mind map or a text document or draft slides.

If your proposal involves speaking about a library / tool / software that you intend to open source in future, the proposal will be considered only when the library / tool / software in question is made open source.

We will notify you about the status of your proposal within two-three weeks of submission.

Selected speakers have to participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you prepare for speaking at the conference.

There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).

Commitment to open source #

HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.

Key dates and deadlines #

Proposal submission deadline: 31 May 2016
Schedule announcement: 15 June 2016
Conference dates: 1 July 2016

##Venue
CMR Institute of Technology, Bangalore

##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

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This submission has been added to the schedule

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

Submitted May 11, 2016

Section: Crisp talk Technical level: Advanced

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.