  Image captioning models typically follow an encoder-decoder architecture
which uses abstract image feature vectors as input to the encoder. One of the
most successful algorithms uses feature vectors extracted from the region
proposals obtained from an object detector. In this work we introduce the
Object Relation Transformer, that builds upon this approach by explicitly
incorporating information about the spatial relationship between input detected
objects through geometric attention. Quantitative and qualitative results
demonstrate the importance of such geometric attention for image captioning,
leading to improvements on all common captioning metrics on the MS-COCO
dataset.
