International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image region entropy: a measure of "visualness" of web images associated with one concept
Proceedings of the 13th annual ACM international conference on Multimedia
Attribute-based vehicle search in crowded surveillance videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Optimization of robust loss functions for weakly-labeled image taxonomies: an imagenet case study
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Towards measuring the visualness of a concept
Proceedings of the 21st ACM international conference on Information and knowledge management
Describing clothing by semantic attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Attribit: content creation with semantic attributes
Proceedings of the 26th annual ACM symposium on User interface software and technology
Proceedings of the 21st ACM international conference on Multimedia
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We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.