Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

With the increasing popularity of posting multimodal messages online, many recent studies
have been carried out utilizing both textual and visual information for multi-modal sarcasm …

Multi-modal named entity recognition (MNER) aims to discover named entities in free text
and classify them into pre-defined types with images. However, dominant MNER models do …

Change captioning aims to describe the semantic change between two similar images. In
this process, as the most typical distractor, viewpoint change leads to the pseudo changes …

Previous work on multimodal machine translation (MMT) has focused on the way of
incorporating vision features into translation but little attention is on the quality of vision …

Multimodal relation extraction is a critical task in information extraction, aiming to predict the
class of relations between head and tail entities from linguistic sequences and related …

Sign language translation (SLT) is a challenging weakly supervised task without word-level
annotations. An effective method of SLT is to leverage multimodal complementarity and to …