# Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking

## TL;DR

This paper explores using generative adversarial networks to learn multimodal representations for video hyperlinking, demonstrating their superiority over autoencoder-based methods and enabling visualization of crossmodal translations for interpretability.

## Contribution

It introduces a novel application of GANs for multimodal representation learning in video hyperlinking, improving over existing autoencoder-based approaches and enhancing interpretability.

## Key findings

- GANs produce better multimodal representations than autoencoders.
- GAN-based models enable visualization of crossmodal translations.
- The approach improves video hyperlinking performance.

## Abstract

Continuous multimodal representations suitable for multimodal information retrieval are usually obtained with methods that heavily rely on multimodal autoencoders. In video hyperlinking, a task that aims at retrieving video segments, the state of the art is a variation of two interlocked networks working in opposing directions. These systems provide good multimodal embeddings and are also capable of translating from one representation space to the other. Operating on representation spaces, these networks lack the ability to operate in the original spaces (text or image), which makes it difficult to visualize the crossmodal function, and do not generalize well to unseen data. Recently, generative adversarial networks have gained popularity and have been used for generating realistic synthetic data and for obtaining high-level, single-modal latent representation spaces. In this work, we evaluate the feasibility of using GANs to obtain multimodal representations. We show that GANs can be used for multimodal representation learning and that they provide multimodal representations that are superior to representations obtained with multimodal autoencoders. Additionally, we illustrate the ability of visualizing crossmodal translations that can provide human-interpretable insights on learned GAN-based video hyperlinking models.