Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Deep vision multimodal learning aims at combining deep visual representation learning with
other modalities, such as text, sound, and data collected from other sensors. With the fast …

We propose a simple solution to use a single Neural Machine Translation (NMT) model to
translate between multiple languages. Our solution requires no changes to the model …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

In this paper, we extend an attention-based neural machine translation (NMT) model by
allowing it to access an entire training set of parallel sentence pairs even after training. The …

In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way,
mulitlingual neural machine translate that enables zero-resource machine translation. When …

This paper introduces and summarises the findings of a new shared task at the intersection
of Natural Language Processing and Computer Vision: the generation of image descriptions …

We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive
decoder naturally incorporates spatial visual features obtained using pre-trained …

Modeling attention in neural multi-source sequence-to-sequence learning remains a
relatively unexplored area, despite its usefulness in tasks that incorporate multiple source …

We propose a simple modification to existing neural machine translation (NMT) models that
enables using a single universal model to translate between multiple languages while …