A statistical approach to machine translation
Computational Linguistics
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
BiTAM: bilingual topic AdMixture models for word alignment
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Automatic attribute discovery and characterization from noisy web data
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Describing people: A poselet-based approach to attribute classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a framework to discover a lexicon of visual attributes that supports fine-grained visual discrimination. It consists of a novel annotation task where annotators are asked to describe differences between pairs of images. This captures the intuition that for a lexicon to be useful, it should achieve twin goals of discrimination and communication. Next, we show that such comparative text collected for many pairs of images can be analyzed to discover topics that encode nouns and modifiers, as well as relations that encode attributes of parts. The model also provides an ordering of attributes based on their discriminative ability, which can be used to create a shortlist of attributes to collect for a dataset. Experiments on Caltech-UCSD birds, PASCAL VOC person, and a dataset of airplanes, show that the discovered lexicon of parts and their attributes is comparable to those created by experts.