Learning visual features for relational CBIR

Abstract

Recent works in deep-learning research highlighted remarkable relational reasoning capabilities of some carefully designed architectures. In this work, we employ a relationship-aware deep learning model to extract compact visual features used relational image descriptors. In particular, we are interested in relational content-based image retrieval (R-CBIR), a task consisting in finding images containing similar inter-object relationships. Inspired by the relation networks (RN) employed in relational visual question answering (R-VQA), we present novel architectures to explicitly capture relational information from images in the form of network activations that can be subsequently extracted and used as visual features. We describe a two-stage relation network module (2S-RN), trained on the R-VQA task, able to collect non-aggregated visual features. Then, we propose the aggregated visual features relation network (AVF-RN) module that is able to produce better relationship-aware features by learning the aggregation directly inside the network. We employ an R-CBIR ground-truth built by exploiting scene-graphs similarities available in the CLEVR dataset in order to rank images in a relational fashion. Experiments show that features extracted from our 2S-RN model provide an improved retrieval performance with respect to standard non-relational methods. Moreover, we demonstrate that the features extracted from the novel AVF-RN can further improve the performance measured on the R-CBIR task, reaching the state-of-the-art on the proposed dataset.