  For many computer vision applications such as image captioning, visual
question answering, and person search, learning discriminative feature
representations at both image and text level is an essential yet challenging
problem. Its challenges originate from the large word variance in the text
domain as well as the difficulty of accurately measuring the distance between
the features of the two modalities. Most prior work focuses on the latter
challenge, by introducing loss functions that help the network learn better
feature representations but fail to account for the complexity of the textual
input. With that in mind, we introduce TIMAM: a Text-Image Modality Adversarial
Matching approach that learns modality-invariant feature representations using
adversarial and cross-modal matching objectives. In addition, we demonstrate
that BERT, a publicly-available language model that extracts word embeddings,
can successfully be applied in the text-to-image matching domain. The proposed
approach achieves state-of-the-art cross-modal matching performance on four
widely-used publicly-available datasets resulting in absolute improvements
ranging from 2% to 5% in terms of rank-1 accuracy.
