Visual interactivity understanding within visual scenes presents a significant challenge in
computer vision. Existing methods focus on complex interactivities while leveraging a simple …

Significant progress has been made in video captioning in recent years. However, most
existing methods directly learn from all given captions without distinguishing the styles of …

The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for
effective knowledge editing. Current methods, including intrinsic knowledge editing and …

Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language
visual and acoustic modalities. The inherent intent ambiguity makes it challenging to …

Research on continual learning in multi-modal tasks has been receiving increasing
attention. However, most existing work overlooks the explicit cross-modal and cross-task …

Research on M ulti-modal L arge L anguage M odel s (MLLMs) towards the multi-image
cross-modal instruction has received increasing attention and made significant progress …

In the field of machine learning, continual learning is a crucial concept that allows models to
adapt to non-stationary data distributions. However, most of the existing works focus on uni …

Video scene graph generation (VidSGG) has emerged as a transformative approach to
capturing and interpreting the intricate relationships among objects and their temporal …

Describing video content according to users' needs is a long-held goal. Although existing
video captioning methods have made significant progress, the generated captions may not …

Understanding human emotions is fundamental to enhancing human-computer interaction,
especially for embodied agents that mimic human behavior. Traditional emotion analysis …