NDLI: Picture: A probabilistic programming language for scene perception

Content Provider	IEEE Xplore Digital Library
Author	Kulkarni, T.D. Kohli, P. Tenenbaum, J.B. Mansinghka, V.
Copyright Year	2015
Description	Author affiliation: MIT, Cambridge, MA, USA (Kulkarni, T.D.; Tenenbaum, J.B.; Mansinghka, V.) \|\| Microsoft Res., Beijing, China (Kohli, P.)
Abstract	Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or “analysis-by-synthesis” approaches, can capture rich scene structure but have been less widely applied than their discriminative counterparts, as they often require considerable problem-specific engineering in modeling and inference, and inference is typically seen as requiring slow, hypothesize-and-test Monte Carlo methods. Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose inference machinery. Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed images by matching not simply pixels, but also more abstract features (e.g., contours, deep neural network activations). Inference can flexibly integrate advanced Monte Carlo strategies with fast bottom-up data-driven methods. Thus both representations and inference strategies can build directly on progress in discriminatively trained systems to make generative vision more robust and efficient. We use Picture to write programs for 3D face analysis, 3D human pose estimation, and 3D object reconstruction - each competitive with specially engineered baselines.
Starting Page	4390
Ending Page	4399
File Size	1553332
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299068
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probabilistic logic Proposals Shape Cameras Analytical models Rendering (computer graphics) Nose
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning invariance through imitation

Dynamic Probabilistic Volumetric Models

A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

Shape-time photography

Real-Time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera

Accidental pinhole and pinspeck cameras: Revealing the scene outside the picture

Effective Acquisition of Dense Anisotropic BRDF

A generic rendering system

Probabilistic simultaneous pose and non-rigid shape recovery

Picture: A probabilistic programming language for scene perception

Similar Documents

Learning invariance through imitation

Dynamic Probabilistic Volumetric Models

A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

Shape-time photography

Real-Time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera

Accidental pinhole and pinspeck cameras: Revealing the scene outside the picture

Effective Acquisition of Dense Anisotropic BRDF

A generic rendering system

Probabilistic simultaneous pose and non-rigid shape recovery

Picture: A probabilistic programming language for scene perception