Attributes for classifier feedback
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Describing clothing by semantic attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Augmented attribute representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Attribute discovery via predictable discriminative binary codes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Attribute learning for understanding unstructured social activity
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Learning compact visual attributes for large-scale image classification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Unsupervised learning of discriminative relative visual attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Cascade: crowdsourcing taxonomy creation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards zero-shot learning for human activity recognition using semantic attribute sequence model
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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
Beyond bag of words: image representation in sub-semantic space
Proceedings of the 21st ACM international conference on Multimedia
ObjectPatchNet: Towards scalable and semantic image annotation and retrieval
Computer Vision and Image Understanding
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Human-nameable visual attributes offer many advantages when used as mid-level features for object recognition, but existing techniques to gather relevant attributes can be inefficient (costing substantial effort or expertise) and/or insufficient (descriptive properties need not be discriminative). We introduce an approach to define a vocabulary of attributes that is both human understandable and discriminative. The system takes object/scene-labeled images as input, and returns as output a set of attributes elicited from human annotators that distinguish the categories of interest. To ensure a compact vocabulary and efficient use of annotators' effort, we 1) show how to actively augment the vocabulary such that new attributes resolve inter-class confusions, and 2) propose a novel "nameability" manifold that prioritizes candidate attributes by their likelihood of being associated with a nameable property. We demonstrate the approach with multiple datasets, and show its clear advantages over baselines that lack a nameability model or rely on a list of expert-provided attributes.