Multimodal fusion focuses on integrating information from multiple modalities with the goal of
more accurate prediction, which has achieved remarkable progress in a wide range of …

Abstract Vision-Language Transformers (VLTs) have shown great success recently but are
meanwhile accompanied by heavy computation costs where a major reason can be …

Abstract Existing Knowledge Distillation (KD) methods typically focus on transferring
knowledge from a large-capacity teacher to a low-capacity student model achieving …

Abstract Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against
presentation attacks. With advancements in sensor manufacture and multi-modal learning …

Protein functions are characterized by interactions with proteins, drugs, and other
biomolecules. Understanding these interactions is essential for deciphering the molecular …

Multimodal learning which integrates data from diverse sensory modes plays a pivotal role
in artificial intelligence. However existing multimodal learning methods often struggle with …

Multimodal learning falls into the trap of the optimization dilemma due to the modality
imbalance phenomenon, leading to unsatisfactory performance in real applications. A core …

As a fundamental problem in multimodal learning multimodal fusion aims to compensate for
the inherent limitations of a single modality. One challenge of multimodal fusion is that the …

One primary topic of multimodal learning is to jointly incorporate heterogeneous information
from different modalities. However most models often suffer from unsatisfactory multimodal …

Test-time adaptation (TTA) has emerged as a new paradigm for reconciling distribution shifts
across domains without accessing source data. However, existing TTA methods mainly …