Learning a Recurrent Residual Fusion Network for Multimodal Matching

Abstract

A major challenge in matching between vision and language is that they typically have completely different features and representations. In this work, we introduce a novel bridge between the modality-specific representations by creating a co-embedding space based on a recurrent residual fusion (RRF) block. Specifically, RRF adapts the recurrent mechanism to residual learning, so that it can recursively improve feature embeddings while retaining the shared parameters. Then, a fusion module is used to integrate the intermediate recurrent outputs and generates a more powerful representation. In the matching network, RRF acts as a feature enhancement component to gather visual and textual representations into a more discriminative embedding space where it allows to narrow the cross-modal gap between vision and language. Moreover, we employ a bi-rank loss function to enforce separability of the two modalities in the embedding space. In the experiments, we evaluate the proposed RRF-Net using two multi-modal datasets where it achieves state-of-the-art results.