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What and Who
Title:From CAD models to neural networks: Learning mid-level image representations for visual recognition
Speaker:Josef Sivic
coming from:Inria Paris, Departement d'Informatique, Ecole Normale Superieure
Speakers Bio:Josef Sivic received a degree from the Czech Technical University,

Prague, in 2002 and PhD from the University of Oxford in
2006. His thesis dealing with efficient visual search of images and
videos was awarded the British Machine Vision Association 2007
Sullivan Thesis Prize and was short listed for the British Computer
Society 2007 Distinguished Dissertation Award. His research interests
include visual search and object recognition applied to large image
and video collections. After spending six months as a postdoctoral
researcher in the Computer Science and Artificial Intelligence
Laboratory at the Massachusetts Institute of Technology, he currently
holds a permanent position as an INRIA researcher at the Departement
d'Informatique, Ecole Normale Superieure, Paris.
He has published over 40 scientific publications and serves as an
Associate Editor for the International Journal of Computer Vision.
He has been awarded an ERC Starting grant in 2013.

Event Type:Talk
Visibility:D2, D4, MMCI
We use this to send out email in the morning.
Level:AG Audience
Language:English
Date, Time and Location
Date:Monday, 28 July 2014
Time:13:00
Duration:60 Minutes
Location:Saarbrücken
Building:E1 4
Room:024
Abstract
In this talk, I will describe our recent work on developing learnable
mid-level representations for instance-level and category-level visual
recognition.

First, I will review our recently developed representation of 3D
scenes where an entire architectural site
is summarized by a set of scene parts learnt in a discriminative
fashion from rendered views of its 3D model. We demonstrate
recognizing 3D scene instances in challenging historical and
non-photographic imagery, such as paintings and drawings, where
standard local invariant features fail.

Second, using a similar approach we show that an object category can
be non-parametrically modeled by a large collection of 3D CAD models
explicitly representing the variation in style and viewpoint. Object
detection in images is posed as a type of 2D to 3D alignment
accomplished by matching mid-level object parts learnt from
synthesized views. We demonstrate detection and alignment of ``chairs"
in challenging Pascal VOC 2012 images using a reference library of
1,394 CAD models downloaded from the Internet.

Finally, we investigate learning and transferring mid-level image
representations using convolutional neural networks. We demonstrate
that an image representation learnt on a task with a large amount of
fully labelled imagery can significantly improve visual recognition
performance on related tasks where supervision is scarce. The proposed
model achieves state-of-the-art results on the Pascal VOC image
classification and action recognition challenge.

The talk is based on recent papers:
- M. Aubry, B. Russell and J. Sivic, Painting-to-3D Model Alignment
Via Discriminative Visual Elements, ACM Transactions on Graphics, 2014
- M. Aubry, D. Maturana, A. Efros, B. Russell and J. Sivic, Seeing 3D
chairs: exemplar part-based 2D-3D alignment using a large dataset of
CAD models, CVPR 2014
- M. Oquab, L. Bottou, I. Laptev and J. Sivic, Learning and
Transferring Mid-Level Image Representations using Convolutional
Neural Networks, CVPR 2014
Contact
Name(s):Michael Stark
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  • Michael Stark, 07/22/2014 04:22 PM -- Created document.