AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Relative Attributes for Enhanced Human-Machine Communication
Devi Parikh, Adriana Kovashka, Amar Parkash, Kristen Grauman

Last modified: 2012-07-14


We propose to model relative attributes 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, providing the richer language needed to fluidly "teach" a system about visual concepts.


attributes, zero-shot learning, image description, feedback, image search, active learning

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