This paper offers an insightful examination of how currently top-trending AI technologies, ie,
generative artificial intelligence (Generative AI) and large language models (LLMs), are …

Most existing text-video retrieval methods focus on cross-modal matching between the
visual content of videos and textual query sentences. However, in real-world scenarios …

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is
an important topic in computer vision research. Along with the growth of computational …

Video classification has achieved remarkable success in recent years, driven by advanced
deep learning models that automatically categorize video content. This paper provides a …

Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery,
and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of diverse and …

Recently, large-scale pre-trained language-image models like CLIP have shown
extraordinary capabilities for understanding spatial contents, but naively transferring such …

Recently, visual-language navigation (VLN)--entailing robot agents to follow navigation
instructions--has shown great advance. However, existing literature put most emphasis on …

Abstract We present Integrated Multimodal Perception (IMP), a simple and scalable
multimodal multi-task training and modeling approach. IMP integrates multimodal inputs …

This paper does not present a novel method. Instead, it delves into an essential, yet must-
know baseline in light of the latest advancements in Generative Artificial Intelligence …

Temporal modeling plays a crucial role in understanding video content. To tackle this
problem, previous studies built complicated temporal relations through time sequence …