Publications

 
 
 
2017
D. P. Papadopoulos, J. R. R. Uijlings, F. Keller and V. Ferrari
Extreme clicking for efficient object annotation
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.

Supplementary material
Also available on arXiv
V. Kalogeiton, P. Weinzaepfel, V. Ferrari and C. Schmid
Action Tubelet Detector for Spatio-Temporal Action Localization
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.

Also available on arXiv
V. Kalogeiton, P. Weinzaepfel, V. Ferrari and C. Schmid
Joint learning of object and action detectors
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.
B. Liu and V. Ferrari
Active Learning for Human Pose Estimation
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.
M. Shi, H. Caesar, V. Ferrari
Weakly Supervised Object Localization Using Things and Stuff Transfer
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017.

Supplementary material
Also available on arXiv
D. Modolo, V. Ferrari
Learning Semantic Part-Based Models from Google Images
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), August 2017

Also available on arXiv
D. P. Papadopoulos, J. R. R. Uijlings, F. Keller and V. Ferrari
Training object class detectors with click supervision
IEEE Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, July 2017. (spotlight oral)

Project page with data
Spotlight video at CVPR 2017
Creative AI podcast interview
Also available on arXiv
A. Gonzalez-Garcia, D. Modolo, V. Ferrari
Objects as Context for Part Detection
arXiv preprint, March 2017.
H. Caesar, J. Uijlings, V. Ferrari
COCO-Stuff: Thing and Stuff Classes in Context
arXiv preprint, March 2017.
L. Del Pero, S. Ricco, R. Sukthankar, V. Ferrari
Behavior Discovery and Alignment of Articulated Object Classes from Unstructured Video
International Journal of Computer Vision (IJCV), 121(2), p. 303-325, January 2017.


Project page

Dataset

Also available on arXiv
2016
C. Silberer, V. Ferrari, M. Lapata
Visually Grounded Meaning Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), December 2016.
A. Gonzalez-Garcia, D. Modolo, V. Ferrari
Do Semantic Parts Emerge in Convolutional Neural Networks?
arXiv preprint, October 2016 (new, substantially updated version).
P. Henderson, V. Ferrari
End-to-end training of object class detectors for mean average precision
Asian Conference on Computer Vision (ACCV), Taipei, Taiwan, November 2016.

Also available on arXiv
A. Papazoglou, L. Del Pero and V. Ferrari
Video temporal alignment for object viewpoint
Asian Conference on Computer Vision (ACCV), Taipei, Taiwan, November 2016.

Dataset
A. Bearman, O. Russakovsky, V.Ferrari and L. Fei-Fei
What's the point: Semantic segmentation with point supervision
European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.

Project page with data
Also available on arXiv
P. Henderson and V. Ferrari
Automatically selecting inference algorithms for discrete energy minimisation
European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.

Also available on arXiv
M. Shi, V. Ferrari
Weakly Supervised Object Localization Using Size Estimates
European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.

Also available on arXiv
H. Caesar, J. Uijlings, V. Ferrari
Region-based semantic segmentation with end-to-end training
European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.

Source code and trained models
Also available on arXiv
A. Papazoglou, L. Del Pero and V. Ferrari
Discovering object aspects from video
Image and Vision Computing, August 2016

Dataset

V. Kalogeiton, V. Ferrari and C. Schmid
Analysing domain shift factors between videos and images for object detection
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), April 2016

Youtube-Objects dataset v 2.0

Also available on arXiv
L. Del Pero, S. Ricco, R. Sukthankar, V. Ferrari
Discovering the physical parts of an articulated object class from multiple videos
IEEE Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.


Project page with dataset

An article on TechCrunch about this work
R. T. Ionescu, B. Alexe, M. Leordeanu, M. Popescu, D. P. Papadopoulos, V. Ferrari
How hard can it be? Estimating the difficulty of visual search in an image
IEEE Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.

Project page with data and code
D. P. Papadopoulos, J. R. R. Uijlings, F. Keller and V. Ferrari
We don't need no bounding-boxes: Training object class detectors using only human verification
IEEE Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016. (spotlight oral)

Also available on arXiv
2015
A. Vezhnevets and V. Ferrari
Object localization in ImageNet by looking out of the window
British Machine Vision Conference (BMVC), Swansea, September 2015.


Also available on arXiv
D. Modolo, A. Vezhnevets, and V. Ferrari
Context Forest for Object Class Detection
British Machine Vision Conference (BMVC), Swansea, September 2015. (oral)


Earlier version available on arXiv
H. Caesar, J. Uijlings and V. Ferrari
Joint Calibration for Semantic Segmentation
British Machine Vision Conference (BMVC), Swansea, September 2015.


Update Aug 2015: Added VGG16 experiments which greatly improves results

Also available on arXiv
M. Volpi and V. Ferrari
Semantic segmentation of urban scenes by learning local class interactions
IEEE Computer Vision and Pattern Recognition Workshops (CVPRW) "EARTHVISION", Boston, June 2015. (Best Paper Award)

Dataset page
L. Del Pero, S. Ricco, R. Sukthankar and V. Ferrari
Articulated Motion Discovery using Pairs of Trajectories
IEEE Computer Vision and Pattern Recognition (CVPR), Boston, June 2015.

Project page
TigDog Dataset V 1.0

Also available on arXiv

J.R.R. Uijlings and V. Ferrari
Situational Object Boundary Detection
IEEE Computer Vision and Pattern Recognition (CVPR), Boston, June 2015. (oral)

Also available on arXiv
A. Gonzalez-Garcia, A. Vezhnevets and V. Ferrari
An Active Search Strategy for Efficient Object Class Detection
IEEE Computer Vision and Pattern Recognition (CVPR), Boston, June 2015.

Also available on arXiv
D. Modolo, A. Vezhnevets, O. Russakovsky and V. Ferrari
Joint calibration of Ensemble of Exemplar SVMs
IEEE Computer Vision and Pattern Recognition (CVPR), Boston, June 2015.


Also available on arXiv
M. Volpi and V. Ferrari
Structured prediction for urban scene semantic segmentation with geographic context
Joint Urban Remote Sensing Event, Lausanne, Switzerland, April 2015.
2014
ImageNet Auto-annotation with Segmentation PropagationM. Guillaumin, D. Kuettel, and V. Ferrari
ImageNet Auto-annotation with Segmentation Propagation
International Journal of Computer Vision (IJCV), 110(3), p. 328-348, December 2014.

Source code for segmentation transfer
Project page with data
Invited talk at Google
D. P. Papadopoulos, A. D. F. Clarke, F. Keller and V. Ferrari
Training object class detectors from eye tracking data
European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014.

Pascal Objects Eye Tracking Dataset
A. Kolesnikov, M. Guillaumin, V. Ferrari and C. H. Lampert
Closed-Form Approximate CRF Training for Scalable Image Segmentation
European Conference on Computer Vision (ECCV), Zurich, Switzerland, September 2014.
A. Vezhnevets and V. Ferrari
Associative embeddings for large-scale knowledge transfer with self-assessment
IEEE Computer Vision and Pattern Recognition (CVPR), Columbus, June 2014.

Also available on arXiv
Project page with data
Detecting people looking at each other in videosM. J. Marin-Jimenez, A. Zisserman, M. Eichner and V. Ferrari
Detecting People Looking at Each Other in Videos
International Journal of Computer Vision (IJCV), 106(3), p. 282-296, February 2014.
2013
Fast object segmentation in unconstrained videoA. Papazoglou and V. Ferrari
Fast object segmentation in unconstrained video
International Conference on Computer Vision (ICCV), Sydney, Australia, December 2013

Project page
learnt unary and pairwise potentialsD. Kuettel and V. Ferrari
Learning to approximate global shape priors for figure-ground segmentation
British Machine Vision Conference (BMVC), Bristol, September 2013. (Best poster prize)
Models of Semantic Representation with Visual AttributesC. Silberer, V. Ferrari and M. Lapata
Models of Semantic Representation with Visual Attributes
Association for Computational Linguistics (ACL), Sofia, August 2013.
imageA. Prest, V. Ferrari, and C. Schmid
Explicit modeling of human-object interactions in realistic videos
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 35(4), pp. 835-848, August 2013.

This publication is a revised version of the homonymous INRIA technical report that appeared in September 2011.
Fast Energy Minimization using Learned State FiltersM. Guillaumin, L. Van Gool and V. Ferrari
Fast Energy Minimization using Learned State Filters
IEEE Computer Vision and Pattern Recognition (CVPR), Portland, June 2013.

Code
2012
Searching for objects driven by contextB. Alexe, N. Heess, Y.W. Teh and V. Ferrari
Searching for objects driven by context
Advances in Neural Information Processing Systems (NIPS), Nevada, USA, December 2012 (spotlight oral).

Sequences of hypotheses generated on Pascal VOC 2010
Appearance Sharing for Collective Human Pose EstimationM. Eichner, V. Ferrari
Appearance Sharing for Collective Human Pose Estimation
Asian Conference on Computer Vision (ACCV), Daejeon, Korea, November 2012.
Has my Algorithm Succeeded? An Evaluator for Human Pose EstimatorsN. Jammalamadaka, A. Zisserman, M. Eichner, V. Ferrari, C. V. Jawahar
Has my Algorithm Succeeded? An Evaluator for Human Pose Estimators
European Conference on Computer Vision (ECCV), Firenze, Italy, October 2012.
Segmentation Propagation in ImageNetD. Kuettel, M. Guillaumin, and V. Ferrari
Segmentation Propagation in ImageNet
European Conference on Computer Vision (ECCV), Firenze, Italy, October 2012. (BEST PAPER AWARD)

Spotlight video at ECCV12
Source code for segmentation transfer
Project page with data
Invited talk at Google
Weakly supervised learning of interactions between humans and objectsA. Prest, C. Schmid, and V. Ferrari
Weakly supervised learning of interactions between humans and objects
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), March 2012.
Weakly supervised localization and learning with generic knowledgeT. Deselaers, B. Alexe, and V. Ferrari
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision (IJCV), 100(3), p. 257-293, September 2012

This publication is a revised version of ETHZ technical report #275 that appeared in August 2011.
Human Pose Co-Estimation and ApplicationsM. Eichner and V. Ferrari
Human Pose Co-Estimation and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 34(11), p. 2282-2288, November 2012.

This publication is a revised version of the homonymous ETHZ technical report #277 that appeared in November 2011.
Synchronic activities stickmen dataset.
Figure-ground segmentation by transferring window masksD. Kuettel and V. Ferrari
Figure-ground segmentation by transferring window masks
IEEE Computer Vision and Pattern Recognition (CVPR), Providence, June 2012.

source code and data
Large-scale Knowledge Transfer for Object Localization in ImageNetM. Guillaumin and V. Ferrari
Large-scale Knowledge Transfer for Object Localization in ImageNet
IEEE Computer Vision and Pattern Recognition (CVPR), Providence, June 2012.

Project page with data
Invited talk at Google
Learning Object Class Detectors from Weakly Annotated VideoA. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari
Learning Object Class Detectors from Weakly Annotated Video
IEEE Computer Vision and Pattern Recognition (CVPR), Providence, June 2012.

Youtube-Objects dataset v 2.0
Weakly Supervised Structured Output Learning for Semantic SegmentationA.Vezhnevets, V. Ferrari, J. M. Buhmann
Weakly Supervised Structured Output Learning for Semantic Segmentation
IEEE Computer Vision and Pattern Recognition (CVPR), Providence, June 2012. (oral)
Active Learning for Semantic Segmentation with Expected ChangeA.Vezhnevets, J. M. Buhmann, V. Ferrari
Active Learning for Semantic Segmentation with Expected Change
IEEE Computer Vision and Pattern Recognition (CVPR), Providence, June 2012.
Video Retrieval by Mimicking PosesN. Jammalamadaka, A. Zisserman, M. Eichner, V. Ferrari, C. V. Jawahar
Video Retrieval by Mimicking Poses
International Conference on Multimedia Retrieval (ICMR), Hong Kong, June 2012.
Articulated Human Pose Estimation and Search in  (Almost) Unconstrained Still ImagesM. Eichner, M. Marin-Jimenez, A. Zisserman, V. Ferrari
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
International Journal of Computer Vision, (IJCV), 99(2), p. 190-214, September 2012

This publication is a revised version of the homonymous ETHZ technical report #272 that appeared in September 2010.
Measuring the objectness of image windowsB. Alexe, T. Deselaers and V. Ferrari
Measuring the objectness of image windows
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 34(11), p. 2189-2202, November 2012.

This publication is a revised version of the homonymous ETHZ technical report #276 that appeared in August 2011.
Source code v2.2
D. Kuettel, M. Guillaumin, and V. Ferrari
Combining Image-Level and Segment-Level Models for Automatic Annotation
18th International Conference on MultiMedia Modelling (MMM), Klagenfurt, Austria, January, 2012. (oral)
2011
Exploiting spatial overlap to efficiently compute appearance distances between image windowsB. Alexe, V. Petrescu, and V. Ferrari
Exploiting spatial overlap to efficiently compute appearance distances between image windows
Advances in Neural Information Processing Systems (NIPS), Granada, December 2011.

Supplementary material
Source code v1.0
imageA. Vezhnevets, V. Ferrari, and J. Buhmann
Weakly Supervised Semantic Segmentation with a Multi-image Model
International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011
imageM. Marin-Jimenez, A. Zisserman, and V. Ferrari
"Here's looking at you, kid" - Detecting people looking at each other in videos
British Machine Vision Conference (BMVC), Dundee, September 2011. (oral)

Data annotations
imageM. Ozcan, L. Jie, V. Ferrari, and B. Caputo
A Large-Scale Database of Images and Captions for Automatic Face Naming
British Machine Vision Conference (BMVC), Dundee, September 2011. (oral)

FAN-Lage database of 125000 images and captions
Learning from Images with CaptionsL. Jie, O. Francesco, C. Barbara, and V. Ferrari
Learning from Images with Captions Using the Maximum Margin Set Algorithm
IDIAP Research Report #30, August 2011 (submitted to PAMI).
Visual and Semantic Similarity in ImageNetT. Deselaers and V. Ferrari
Visual and Semantic Similarity in ImageNet
IEEE Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011.
2010
Localizing Objects while Learning Their Appearance T. Deselaers, B. Alexe, and V. Ferrari
Localizing Objects while Learning Their Appearance
European Conference on Computer Vision (ECCV), Crete, Greece, September 2010. (oral)

This publication is also ETH technical report #274.
ClassCut for Unsupervised Class SegmentationB. Alexe, T. Deselaers, and V. Ferrari
ClassCut for Unsupervised Class Segmentation
European Conference on Computer Vision (ECCV), Crete, Greece, September 2010.
We Are Family: Joint Pose Estimation of Multiple PersonsM. Eichner and V. Ferrari
We Are Family: Joint Pose Estimation of Multiple Persons
European Conference on Computer Vision (ECCV), Crete, Greece, September 2010.

supplementary material
Dataset
A Conditional Random Field for Multiple-Instance LearningT. Deselaers and V. Ferrari
A Conditional Random Field for Multiple-Instance Learning
International Conference on Machine Learning (ICML), Haifa, Israel, June 2010.
Paper discussion site
B. Alexe, T. Deselaers, and V. Ferrari
What is an object?
IEEE Computer Vision and Pattern Recognition (CVPR), San Francisco, June 2010.

Source code v2.2
What's going on? Discovering Spatio-Temporal Dependencies in Dynamic ScenesD. Kuettel, M. Breitenstein, L. van Gool, and V. Ferrari
What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes
IEEE Computer Vision and Pattern Recognition (CVPR), San Francisco, June 2010. (oral)

Project page with data

An article about this work in the eth life magazine available in  english and  german.
A short article in the german M.I.T. magazine of 2010/8, see preview or original content (paywalled).
Global and Efficient Self-Similarity for Object Classification and DetectionT. Deselaers and V. Ferrari
Global and Efficient Self-Similarity for Object Classification and Detection
IEEE Computer Vision and Pattern Recognition (CVPR), San Francisco, June 2010. (oral)

Video of the talk at CVPR 2010
Code available
Which Energy Minimization for my MRF/CRF? A Cheat-SheetB. Alexe, T. Deselaers, M. Eichner, V. Ferrari, P. Gehler, A. Lehmann, S. Pellegrini, A. Prest
Which Energy Minimization for my MRF/CRF? A Cheat-Sheet
Computer Vision Laboratory, ETH Zurich, Technical Report 273
From Images to Shape Models for Object Detection -- imageV. Ferrari, F. Jurie, and C. Schmid
From Images to Shape Models for Object Detection
International Journal of Computer Vision (IJCV), March 2010.

Learning explicit shape models from unsegmented training images, and using them to localize object outlines in novel test
images.
Performance plots available as Matlab figures
Matlab source code v1.3
This publication is a revised version of the homonymous INRIA technical report appeared in July 2008 (now obsolete and no longer available).
2009
Who's doing what -- imageL. Jie, B. Caputo, and V. Ferrari
Who's Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation
Advances in Neural Information Processing Systems (NIPS), Vancouver, December 2009.

Associating persons' faces and poses in news images to names and verbs in their captions.
Better appearance models for pictorial structures -- imageM. Eichner and V. Ferrari
Better Appearance Models for Pictorial Structures
British Machine Vision Conference (BMVC), London, September 2009. (oral)

Estimating body part appearance models from a single image of an unknown person.
ETHZ PASCAL Stickmen dataset of annotated 2D human poses
Pose estimation results
Matlab source code
Using Multi-view Recognition to Guide a Robot's attention -- imageA. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, L. Van Gool
Using Multi-view Recognition to Guide a Robot's attention
International Journal of Robotics Research (IJRR), August 2009.

Multi-view object class detection and meta-data inference (this journal paper is an extended version of our CVPR 2006 and RSS 2008 papers)
2D Human Pose Estimation in TV Shows -- imageV. Ferrari, M. Marin, and A. Zisserman
2D Human Pose Estimation in TV Shows
Dagstuhl post-proceedings, 2009.

Fully automatic 2D human pose estimation in uncontrolled video. This is an extension of our CVPR 2008 paper.
Shape-from-recognition: Recognition enables meta-data transfer -- imageA. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, L. Van Gool
Shape-from-recognition: Recognition enables meta-data transfer
Computer Vision and Image Understanding (CVIU), December 2009.

Inferring meta-data, such as depth, surface normals, and part decomposition from a single image of an object, using cognitive feeback from recognition (this journal paper is an extended version of our 3dRR 2007 paper)
imageV. Ferrari, M. Marin, and A. Zisserman
Pose Search: retrieving people using their pose
IEEE Computer Vision and Pattern Recognition (CVPR), Miami, June 2009. (oral)

Retrieving shots containing a particular human pose from movies and TV videos.
Buffy Pose Classes dataset for pose search
Showcase page

publications by V. Ferrari before CALVIN


2008
Progressive Search Space Reduction for Human Pose Estimation -- imageV. Ferrari, M. Marin, and A. Zisserman
Progressive Search Space Reduction for Human Pose Estimation
IEEE Computer Vision and Pattern Recognition (CVPR), Alaska, June 2008.

Fully automatic 2D human pose estimation in uncontrolled video (Buffy the Vampire Slayer!).
Buffy Stickmen dataset of annotated 2D human poses
Software for detecting and tracking human upper-bodies
Showcase page
Using Recognition to Guide a Robot's Attention -- imageA. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, L. Van Gool
Using Recognition to Guide a Robot's Attention
Robotics: Science and Systems Conference (RSS), Zurich, Switzerland, June 2008. (oral)

Recognizing objects of interest for a robot, and localizing interaction points
Groups of Adjacent Contour Segments for Object Detection -- imageV. Ferrari, L. Fevrier, F. Jurie, and C. Schmid
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), January 2008

A new family of local contour features and their application to object class detection.
Matlab source code v1.05
Source code available on request
Performance plots available as Matlab figures
This publication is a revised version of the homonymous INRIA technical report appeared in September 2006 (now obsolete and no longer available).
2007
Learning Visual AttributesV. Ferrari and A. Zisserman
Learning Visual Attributes
Advances in Neural Information Processing Systems (NIPS), Vancouver, December 2007 (spotlight)

Weakly supervised learning of visual attributes, such as 'red' and 'striped'.
Dataset
Showcase page
Depth-From-Recognition: Inferring Meta-data by Cognitive Feedback-- imageA. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, and L. Van Gool
Depth-From-Recognition: Inferring Meta-data by Cognitive Feedback
3D Representation for Recognition (3dRR) - Workshop in conjunction with International Conference on Computer Vision (ICCV), Rio de Janeiro, Brasil, October 2007

Inferring 3D depth from a single image of an object, using cognitive feeback from recognition
Efficient Mining of Frequent and Distinctive Feature Configurations -- imageT. Quack, V. Ferrari, B. Leibe, and L. Van Gool
Efficient Mining of Frequent and Distinctive Feature Configurations
International Conference on Computer Vision (ICCV), Rio de Janeiro, Brasil, October 2007

Feature selection for object class detection, by efficient mining of frequent and distinctive spatial feature configurations
Oxford TRECVid 2007 - Notebook paper -- imageJ. Philbin, O. Chum, J. Sivic, V. Ferrari, M. Marin, A. Bosch, N. Apostolof, and A. Zisserman
Oxford TRECVid 2007 - Notebook paper
Accurate Object Detection with Deformable Shape Models Learnt from Images -- imageV. Ferrari, F. Jurie, and C. Schmid
Accurate Object Detection with Deformable Shape Models Learnt from Images
IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, June 2007.

Performance plots available as Matlab figures
Matlab source code v1.3
Simultaneous Object Recognition and Segmentation by Image Exploration -- imageV. Ferrari, T. Tuytelaars, and L. Van Gool
Simultaneous Object Recognition and Segmentation by Image Exploration
Lecture notes in computer science, vol. 4170, (Toward category-level object recognition, eds. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman), pp. 151-178, 2006

Book chapter in a survey of state-of-the-art object recognition methods.
Source code v1.1
Video Mining with Frequent Itemset Configurations -- imageT. Quack, V. Ferrari, and L. Van Gool
Video Mining with Frequent Itemset Configurations
International Conference on Image and Video Retrieval (CIVR), Arizona, July 2006.
Towards Multi-View Object Class Detection -- imageA. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, B. Schiele, and L. Van Gool
Towards Multi-View Object Class Detection
IEEE Computer Vision and Pattern Recognition (CVPR), New York, June 2006.

Multi-view object class detection by combining my Image Exploration technique with Leibe`s Implict Shape Model.
Object Detection by Contour Segment Networks -- imageV. Ferrari, T. Tuytelaars, and L. Van Gool
Object Detection by Contour Segment Networks
European Conference on Computer Vision (ECCV), Graz, May 2006. (oral)

Detecting object classes in real images, given a single hand-drawn example as model of their shape.
Performance plots available as Matlab figures
ETHZ Shape Classes v1.2 dataset (including our performance plots as matlab figures)
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views -- imageV. Ferrari, T. Tuytelaars, and L. Van Gool
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views
International Journal of Computer Vision (IJCV), April 2006

Special issue with extended versions of 5 papers selected from ECCV 2004; also includes material from the CVPR 2004 paper.
Source code v1.1
2005
Wide-baseline Stereo Matching with Line Segments -- imageH. Bay, V. Ferrari, L. Van Gool
Wide-baseline Stereo Matching with Line Segments
IEEE Computer Vision and Pattern Recognition (CVPR), San Diego, USA, June 2005
Composite Texture Synthesis -- imageA. Zalesny, V. Ferrari, G. Caenen, and L. Van Gool
Composite Texture Synthesis
International Journal of Computer Vision (IJCV), 62:1-2, pp. 161-176, April 2005
2004
Retrieving Objects From Videos Based on Affine Regions -- imageVittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Retrieving Objects From Videos Based on Affine Regions
European Signal Processing conference (EUSIPCO), Vienna, Austria, September 2004 (oral)
Integrating Multiple Model Views for Object Recognition -- imageVittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Integrating Multiple Model Views for Object Recognition
IEEE Computer Vision and Pattern Recognition (CVPR), Washington, USA, June 2004

Source code v1.1
Simultaneous Object Recognition and Segmentation by Image Exploration -- imageVittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Simultaneous Object Recognition and Segmentation by Image Exploration
European Conference on Computer Vision (ECCV), Prague, May 2004. (oral)

ETHZ Toys dataset
Source code v1.1
2003
Fast indexing for image retrieval based on local appearance with re-ranking -- imageH. Shao, T. Svoboda, V. Ferrari, T. Tuytelaars, L. Van Gool
Fast indexing for image retrieval based on local appearance with re-ranking
International Conference on Image Processing (ICIP), September 2003. (oral)
Wide-baseline muliple-view Correspondences -- imageVittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Wide-baseline muliple-view Correspondences
IEEE Computer Vision and Pattern Recognition (CVPR), Madison, USA, June 2003
2002
3D Modeling and Registration Under Wide Baseline Conditions -- imageL. Van Gool, T. Tuytelaars, V. Ferrari, C. Strecha, J. Vanden Wyngaerd and M. Vergauwen
3D Modeling and Registration Under Wide Baseline Conditions
Proc. ISPRS Commission III, Vol.~34, Part 3A, Photogrammetric Computer Vision (PCV), Graz, September 2002, pp.~3-14
Invited Keynote speech

Composite Texture Descriptions -- imageAlexey Zalesny, Vittorio Ferrari, Geert Caenen, Dominik Auf der Maur, and Luc Van Gool
Composite Texture Descriptions
European Conference on Computer Vision (ECCV), Copenhagen, Danemark, May 2002, Vol. 3, pp. 180-194
Analyzing the layout of composite textures -- imageGeert Caenen, Vittorio Ferrari, Alexey Zalesny, and Luc Van Gool
Analyzing the layout of composite textures
Texture 2002 Workshop in conjunction with ECCV, Copenhagen, Danemark, May 2002, pp. 15-19.
Parallel Composite Texture Synthesis -- imageAlexey Zalesny, Vittorio Ferrari, Geert Caenen, and Luc Van Gool
Parallel Composite Texture Synthesis
Texture 2002 Workshop in conjunction with ECCV, Copenhagen, Danemark, May 2002, pp. 151-155.
2001
Real-time Affine Region Tracking and Coplanar Grouping -- imageVittorio Ferrari, Tinne Tuytelaars and Luc Van Gool
Real-time Affine Region Tracking and Coplanar Grouping
in Proc. of the IEEE Computer Vision and Pattern Recognition (CVPR), Kauai, Hawaii, December 2001.
Markerless Augmented Reality with a Real-time Affine Region Tracker -- imageVittorio Ferrari, Tinne Tuytelaars and Luc Van Gool
Markerless Augmented Reality with a Real-time Affine Region Tracker
in Proc. of the IEEE and ACM International Symposium on Augmented Reality (ISAR), New York, New York, October 2001, pp. 87-96 (oral)