  Scene graphs are a powerful structured representation of the underlying
content of images, and embeddings derived from them have been shown to be
useful in multiple downstream tasks. In this work, we employ a graph
convolutional network to exploit structure in scene graphs and produce image
embeddings useful for semantic image retrieval. Different from
classification-centric supervision traditionally available for learning image
representations, we address the task of learning from relative similarity
labels in a ranking context. Rooted within the contrastive learning paradigm,
we propose a novel loss function that operates on pairs of similar and
dissimilar images and imposes relative ordering between them in embedding
space. We demonstrate that this Ranking loss, coupled with an intuitive triple
sampling strategy, leads to robust representations that outperform well-known
contrastive losses on the retrieval task. In addition, we provide qualitative
evidence of how retrieved results that utilize structured scene information
capture the global context of the scene, different from visual similarity
search.
