Describing objects by their attributes

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object ("spotty dog", not just "dog"); to say something about unfamiliar objects ("hairy and four-le...

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Bibliographic Details
Published in:2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 1778 - 1785
Main Authors: Farhadi, Ali, Endres, Ian, Hoiem, Derek, Forsyth, David
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2009
Subjects:
Cats
Computer vision
Detectors
Dogs
Leg
Motorcycles
Object detection
Object recognition
Shape
Testing
ISBN:1424439922, 9781424439928
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object ("spotty dog", not just "dog"); to say something about unfamiliar objects ("hairy and four-legged", not just "unknown"); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic ("spotty") or discriminative ("dogs have it but sheep do not"). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206772