Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Generalization from a Single View in Face Recognition
Generalization from a Single View in Face Recognition
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Fusing object detection and region appearance for image-text alignment
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
Object-Centric spatial pooling for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Ensemble partitioning for unsupervised image categorization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
In defence of negative mining for annotating weakly labelled data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Learning discriminative localization from weakly labeled data
Pattern Recognition
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Learning a new object class from cluttered training images is very challenging when the location of object instances is unknown. Previous works generally require objects covering a large portion of the images. We present a novel approach that can cope with extensive clutter as well as large scale and appearance variations between object instances. To make this possible we propose a conditional random field that starts from generic knowledge and then progressively adapts to the new class. Our approach simultaneously localizes object instances while learning an appearance model specific for the class. We demonstrate this on the challenging PASCAL VOC 2007 dataset. Furthermore, our method enables to train any state-of-the-art object detector in a weakly supervised fashion, although it would normally require object location annotations.