Thanks for a gift to

Microsoft Research

and the overall sponsors

Computer Vision Foundation IEEE Computer Science

Structured Prediction

Tractability, Learning, and Inference

24th June 2013

CVPR 2013 workshop, Portland Oregon

About

Computer vision tasks are routinely addressed by building a statistical model that can be learned from annotated data and then be used to perform inference on novel test data. Rich models that express the real world faithfully most often are intractable in that inference and estimation within the model class are hard problems. On the other hand, scalable and efficient discriminative models are based on simplifying assumptions that ignore important physical constraints; as result, for many tasks high performance can only be achieved with extremely large training sets. Therefore, a key problem in computer vision applications is in constructing models expressive enough to solve the task at hand while remaining tractable. Many successful examples where this has been achieved have led to breakthroughs for computer vision, such as for example graphcut-based image segmentation or deformable part models (DPM).

The goal of this workshop is to bring together researchers from the computer vision and machine learning community to discuss all issues related to tractable structured prediction models. In particular,

  • Model representations
  • Inference
  • Estimation

In all aspects the computer vision community has been at the forefront of developing new ideas, in representation (e.g. perturb-and-MAP, sum-product networks, dense random fields, deep generative models), inference (e.g. dense mean field inference, higher-order factors), and estimation (e.g. direct loss minimization).

Invited Speakers

Important Dates

  • Submission: Friday, April 26th, 2013
  • Author Notification: Monday, May 20th, 2013
  • Final Version of submission: Friday, May 31st, 2013
  • Workshop Date: Monday, June, 24th, 2013 (one day before main conference)

Submission

  • Papers should be in CVPR style
  • Maximum paper length is 6 pages
  • Papers will be reviewed in a double blind process
  • Accepted papers are not published as part of IEEE Proceedings but inofficially on this website
Please use the submission server at

Schedule

9:00-9:05 workshop opening
9:05-9:55 invited talk: Danny Tarlow Designing Loss Functions for Structured Prediction(slides,avi,webm)
9:55-10:20 Ben London Collective Activity Detection using Hinge-loss Markov Random Fields (pdf,slides,avi,webm)
10:20-10:45 coffee break
10:45-11:35 invited talk: Justin Domke Reducing CRF training to a series of (possibly nonlinear) logistic regression problems (slides,avi,webm)
11:35-12:00 Michael Stark Modeling Instance Appearance for Recognition - Can We Do Better Than EM? (pdf,slides,avi,webm)
12:00-14:00 lunch break
14:00-14:50 invited talk: Pedro Felzenszwalb Contour completion with Fields-of-Patterns (slides,avi,webm)
14:50-15:15 Charles Dubout Accelerated Training of Linear Object Detectors (pdf,slides,avi,webm)
15:15-15:55 coffee and tea break
15:55-16:20 Xiaoyu Wang Hierarchical Feature Pooling with Structure Learning: A new method for Pedestrian Detection (slides,avi,webm)
16:20-17:10 invited talk: Raquel Urtasun Efficient learning and inference for holistic scene understanding, (slides,avi,webm)
17:10-17:15 closing remarks

References and Related

Selected References

Related Workshops

PC Committee

  • Karteek Alahari, ENS
  • Bjoern Andres, Harvard
  • Dhruv Batra, TTIC/Virginia Tech
  • Matthew Blaschko, Ecole Centrale Paris
  • Justin Domke, NICTA
  • Jason Eisner, John Hopkins University
  • Pedro Felzenszwalb, Brown
  • Jeremy Jancsary, Microsoft Research
  • Christoph Lampert, IST Austria
  • Andre Martins, Priberam
  • Daniel Munoz, CMU
  • George Papandreou, UCLA
  • Samuel Rota-Bulo, Ca' Foscari University of Venice
  • Benjamin Sapp, Google
  • Danny Tarlow, Microsoft Research
  • Zhuowen Tu, UCLA
  • Raquel Urtasun, TTI Chicago
  • David Weiss, UPenn

Talk Videos

Danny Tarlow

Download: slides avi webm

Ben London

Download: slides avi webm

Justin Domke

Download: slides avi webm

Michael Stark

Download: slides avi webm

Pedro Felzenszwalb

Download: slides avi webm

Charles Dubout

Download: slides avi webm

Xiaoyu Wang

Download: slides avi webm

Raquel Urtasun

Download: slides avi webm