Exemplar-Driven Top-Down Saliency Detection via Deep Association

Top-down saliency detection is a knowledge-driven search task. While some previous methods aim to learn this "knowledge" from category-specific data, others transfer existing annotations in a large dataset through appearance matching. In contrast, we propose in this paper a locateby-exemplar strategy. This approach is challenging, as we only use a few exemplars (up to 4) and the appearances among the query object and the exemplars can be very different. To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects. The first stage is for learning object-to-object association, and the second stage is to learn background discrimination. Extensive experimental evaluations show that the proposed method outperforms different baselines and the category-specific models. In addition, we explore the influence of exemplar properties, in terms of exemplar number and quality. Furthermore, we show that the learned model is a universal model and offers great generalization to unseen objects.