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 …

When trained at sufficient scale, auto-regressive language models exhibit the notable ability
to learn a new language task after being prompted with just a few examples. Here, we …

We present DocFormer-a multi-modal transformer based architecture for the task of Visual
Document Understanding (VDU). VDU is a challenging problem which aims to understand …

Multimodal data collected from the real world are often imperfect due to missing modalities.
Therefore multimodal models that are robust against modal-incomplete data are highly …

We investigate the capability of a transformer pretrained on natural language to generalize
to other modalities with minimal finetuning--in particular, without finetuning of the self …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

Multimodal Sentiment Analysis is an active area of research that leverages multimodal
signals for affective understanding of user-generated videos. The predominant approach …

This work proposes a new challenge set for multimodal classification, focusing on detecting
hate speech in multimodal memes. It is constructed such that unimodal models struggle and …

Large-scale pre-training has brought unimodal fields such as computer vision and natural
language processing to a new era. Following this trend, the size of multimodal learning …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …