Abstract

Given two images, we want to predict which exhibits a particular visual attribute more than the other—even whenthe two images are quite similar. Existing relative attributemethods rely on global ranking functions; yet rarely willthe visual cues relevant to a comparison be constant for all data, nor will humans’ perception of the attribute neces-sarily permit a global ordering. To address these issues,we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challeng-ing datasets—including a large newly curated dataset for fine-grained comparisons—our method outperforms stateof-the-art methods for relative attribute prediction.