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AAAI
2012

Relative Attributes for Enhanced Human-Machine Communication

8 years 3 months ago
Relative Attributes for Enhanced Human-Machine Communication
We propose to model relative attributes1 that capture the relationships between images and objects in terms of human-nameable visual properties. For example, the models can capture that animal A is ‘furrier’ than animal B, or image X is ‘brighter’ than image B. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We show how these relative attribute predictions enable a variety of novel applications, including zero-shot learning from relative comparisons, automatic image description, image search with interactive feedback, and active learning of discriminative classifiers. We overview results demonstrating these applications with images of faces and natural scenes. Overall, we find that relative attributes enhance the precision of communication between humans and computer vision algorithms, provi...
Devi Parikh, Adriana Kovashka, Amar Parkash, Krist
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Devi Parikh, Adriana Kovashka, Amar Parkash, Kristen Grauman
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