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 …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

The dominant paradigm for learning video-text representations--noise contrastive learning--
increases the similarity of the representations of pairs of samples that are known to be …

Language understanding research is held back by a failure to relate language to the
physical world it describes and to the social interactions it facilitates. Despite the incredible …

Multimodal sentiment analysis aims to extract and integrate semantic information collected
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …

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 …

In this work, we investigate a more realistic unsupervised multimodal machine translation
(UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text …

Reliable evaluation benchmarks designed for replicability and comprehensiveness have
driven progress in machine learning. Due to the lack of a multilingual benchmark, however …

Unpaired cross-lingual image captioning has long suffered from irrelevancy and disfluency
issues, due to the inconsistencies of the semantic scene and syntax attributes during …

Vision-and-language pre-training has achieved impressive success in learning multimodal
representations between vision and language. To generalize this success to non-English …