Knowing Who To Listen To: Prioritizing Experts From A Diverse Ensemble For Attribute Personalization

Learning attribute models for applications like Zero-Shot Learning (ZSL) and image search is challenging because they require attribute classifiers to generalize to test data that may be very different from the training data. A typical scenario is when the notion of an attribute may differ from one user to another, e.g. one user may find a shoe formal whereas another user may not. In this case, the distribution of labels at test time is different from that at training time. We argue that due to the uncertainty in what the test distribution might be, committing to one attribute model during training is not advisable. We propose a novel framework for attribute learning which involves training an ensemble of diverse models for attributes and identifying experts from them at test time given a small amount of personalized annotations from a user. Our approach for attribute personalization is not specific to any classification model and we show results using Random Forest and SVM ensembles. We experiment with 2 datasets: SUN Attributes and Shoes and show significant improvements over baselines.