With the aim of promoting and understanding the multilingual version of image search, we leverage visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations. Specifically, our model attends to different types of textual semantics in two languages and visual objects for fine-grained alignments between sentences and images. We introduce a new objective function which explicitly encourages attention diversity to learn an improved visual-semantic embedding space. We evaluate our model in the German-Image and English-Image matching tasks on the Multi30K dataset, and in the Semantic Textual Similarity task with the English descriptions of visual content. Results show that our model yields a significant performance gain over other methods in all of the three tasks.
@article{arxiv.1910.00058,
title = {Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations},
author = {Po-Yao Huang and Xiaojun Chang and Alexander Hauptmann},
journal= {arXiv preprint arXiv:1910.00058},
year = {2019}
}