A Computational Model for Visual Selection, Neural Computation, vol.39, issue.3, 1999. ,
DOI : 10.1038/381520a0
Unsupervised learning of categorical segments in image collections, 2008. ,
Automatic Attribute Discovery and Characterization from Noisy Web Data, ECCV, 2010. ,
DOI : 10.1007/978-3-642-15549-9_48
Poselets: Body part detectors trained using 3D human pose annotations, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459303
The devil is in the details: an evaluation of recent feature encoding methods, Procedings of the British Machine Vision Conference 2011, 2011. ,
DOI : 10.5244/C.25.76
Fine-Grained Crowdsourcing for Fine-Grained Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
DOI : 10.1109/CVPR.2013.81
Fast and balanced: Efficient label tree learning for large scale object recognition, NIPS, 2011. ,
Annotator rationales for visual recognition, 2011 International Conference on Computer Vision, p.5, 2011. ,
DOI : 10.1109/ICCV.2011.6126394
Combining attributes and Fisher vectors for efficient image retrieval, CVPR 2011, p.5, 2011. ,
DOI : 10.1109/CVPR.2011.5995595
URL : https://hal.archives-ouvertes.fr/inria-00566293
The benefits and challenges of collecting richer object annotations, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, 2010. ,
DOI : 10.1109/CVPRW.2010.5543183
The PASCAL visual obiect classes challenge 2007 (VOC2007) results, 2007. ,
Attribute-centric recognition for cross-category generalization, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539924
Describing objects by their attributes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2005. ,
DOI : 10.1109/CVPR.2009.5206772
Every Picture Tells a Story: Generating Sentences from Images, ECCV, 2005. ,
DOI : 10.1007/978-3-642-15561-1_2
A Bayesian approach to unsupervised one-shot learning of object categories, ICCV, issue.3, 2003. ,
Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010. ,
DOI : 10.1109/TPAMI.2009.167
Cascade object detection with deformable part models, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539906
Discriminatively trained deformable part models, release 5, p.5 ,
Diagnosing Error in Object Detectors, ECCV, 2012. ,
DOI : 10.1007/978-3-642-33712-3_25
Shufflets: Shared Mid-level Parts for Fast Object Detection, 2013 IEEE International Conference on Computer Vision, p.7, 2013. ,
DOI : 10.1109/ICCV.2013.176
Whittlesearch: Image search with relative attribute feedback, CVPR, p.5, 2012. ,
Baby talk: Understanding and generating simple image descriptions, CVPR 2011, p.3, 2011. ,
DOI : 10.1109/CVPR.2011.5995466
FaceTracer: A Search Engine for Large Collections of Images with Faces, ECCV, 2008. ,
DOI : 10.1007/978-3-540-88693-8_25
Attribute and simile classifiers for face verification, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459250
Learning to detect unseen object classes by between-class attribute transfer, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.5, 2009. ,
DOI : 10.1109/CVPR.2009.5206594
Detecting objects in large image collections and videos by efficient subimage retrieval, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459359
Discovering a Lexicon of Parts and Attributes, ECCV Workshop, 2012. ,
DOI : 10.1007/978-3-642-33885-4_3
Finegrained visual classification of aircraft, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00842101
NYC3DCars: A Dataset of 3D Vehicles in Geographic Context, 2013 IEEE International Conference on Computer Vision, 2013. ,
DOI : 10.1109/ICCV.2013.99
Probability and computing -randomized algorithms and probabilistic analysis, CUP, issue.6, 2005. ,
Im2text: Describing images using 1 million captioned photographs, NIPS, 2011. ,
Interactively building a discriminative vocabulary of nameable attributes, CVPR 2011, p.5, 2011. ,
DOI : 10.1109/CVPR.2011.5995451
Relative attributes, 2011 International Conference on Computer Vision, 2011. ,
DOI : 10.1109/ICCV.2011.6126281
Attributes for Classifier Feedback, ECCV, 2012. ,
DOI : 10.1007/978-3-642-33712-3_26
Attribute learning in large-scale datasets. ECCV Workshop Parts and Attributes, p.5, 2010. ,
LabelMe: A Database and Web-Based Tool for Image Annotation, International Journal of Computer Vision, vol.3, issue.1, 2005. ,
DOI : 10.1007/s11263-007-0090-8
Image ranking and retrieval based on multi-attribute queries, CVPR 2011, 2011. ,
DOI : 10.1109/CVPR.2011.5995329
Efficient Object Category Recognition Using Classemes, ECCV, p.5, 2010. ,
DOI : 10.1007/978-3-642-15549-9_56
The caltech-ucsd birds-200-2011 dataset, 2011. ,
Joint learning of visual attributes, object classes and visual saliency, 2009 IEEE 12th International Conference on Computer Vision, 2009. ,
DOI : 10.1109/ICCV.2009.5459194
Comparative object similarity for improved recognition with few or no examples, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.5, 2010. ,
DOI : 10.1109/CVPR.2010.5539955
Learning Models for Object Recognition from Natural Language Descriptions, Procedings of the British Machine Vision Conference 2009, 2009. ,
DOI : 10.5244/C.23.2
A Discriminative Latent Model of Object Classes and Attributes, ECCV, p.5, 2010. ,
DOI : 10.1007/978-3-642-15555-0_12
Do We Need More Training Data or Better Models for Object Detection?, Procedings of the British Machine Vision Conference 2012, p.5, 2012. ,
DOI : 10.5244/C.26.80
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.259.7748