Adversarial Representation Learning for Text-to-Image Matching
This work improves cross-modal matching for applications like image captioning and visual question answering, though it is incremental as it builds on existing adversarial and BERT-based methods.
The paper tackled the problem of learning discriminative feature representations for text-to-image matching by addressing both modality distance and textual complexity, achieving state-of-the-art performance with absolute improvements of 2% to 5% in rank-1 accuracy on four datasets.
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.