  The joint understanding of vision and language has been recently gaining a
lot of attention in both the Computer Vision and Natural Language Processing
communities, with the emergence of tasks such as image captioning, image-text
matching, and visual question answering. As both images and text can be encoded
as sets or sequences of elements -- like regions and words -- proper reduction
functions are needed to transform a set of encoded elements into a single
response, like a classification or similarity score. In this paper, we propose
a novel fully-attentive reduction method for vision and language. Specifically,
our approach computes a set of scores for each element of each modality
employing a novel variant of cross-attention, and performs a learnable and
cross-modal reduction, which can be used for both classification and ranking.
We test our approach on image-text matching and visual question answering,
building fair comparisons with other reduction choices, on both COCO and VQA
2.0 datasets. Experimentally, we demonstrate that our approach leads to a
performance increase on both tasks. Further, we conduct ablation studies to
validate the role of each component of the approach.
