Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

This paper focuses on the data-insufficiency problem in multi-task learning within an
episodic training setup. Specifically, we explore the potential of heterogeneous information …

In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …

Prototypical network is a key algorithm to solve few-shot problems. Previous prototypical
network based methods average sentence embeddings of the same class to obtain …

There has been growing concern regarding data privacy during the development and
deployment of Multimodal Foundation Models for Artificial General Intelligence (AGI), while …

Single domain generalization (SDG) aims to train a robust model against unknown target
domain shifts using data from a single source domain. Data augmentation has been proven …

Personalized prediction of responses for individual entities caused by external drivers is vital
across many disciplines. Recent machine learning (ML) advances have led to new state-of …

Visually-rich document entity retrieval (VDER), which extracts key information (eg date,
address) from document images like invoices and receipts, has become an important topic …

Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-
modal data, which is overlooked by previous work in the literature. It is desirable to not only …

Event argument extraction (EAE) is an important information extraction task, which aims to
identify the arguments of an event described in a given text and classify the roles played by …