Just Noticeable Differences in Visual Attributes

Aron Yu, Kristen Grauman; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2416-2424

We explore the problem of predicting "just noticeable differences" in a visual attribute. While some pairs of images have a clear ordering for an attribute (e.g., A is more sporty than B), for others the difference may be indistinguishable to human observers. However, existing relative attribute models are unequipped to infer partial orders on novel data. Attempting to map relative attribute ranks to equality predictions is non-trivial, particularly since the span of indistinguishable pairs in attribute space may vary in different parts of the feature space. We develop a Bayesian local learning strategy to infer when images are indistinguishable for a given attribute. On the UT-Zap50K shoes and LFW-10 faces datasets, we outperform a variety of alternative methods. In addition, we show the practical impact on fine-grained visual search.