Rapid advancements have been made in extending Large Language Models (LLMs) to
Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data …

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language
Models (MLLMs) have garnered increased attention from both industry and academia …

Current datasets for long-form video understanding often fall short of providing genuine long-
form comprehension challenges, as many tasks derived from these datasets can be …

Despite the rapid integration of video perception capabilities into Large Multimodal Models
(LMMs), the underlying mechanisms driving their video understanding remain poorly …

Rapid development of large language models (LLMs) has significantly advanced multimodal
large language models (LMMs), particularly in vision-language tasks. However, existing …

Egocentric videos provide a unique perspective into individuals' daily experiences, yet their
unstructured nature presents challenges for perception. In this paper, we introduce AMEGO …

Video-based multimodal large language models (Video-LLMs) possess significant potential
for video understanding tasks. However, most Video-LLMs treat videos as a sequential set of …

Recent advancements in video-based large language models (Video LLMs) have witnessed
the emergence of diverse capabilities to reason and interpret dynamic visual content …

Long-context modeling is a critical capability for multimodal large language models
(MLLMs), enabling them to process long-form contents with implicit memorization. Despite …

Most existing video understanding benchmarks for multimodal large language models
(MLLMs) focus only on short videos. The limited number of benchmarks for long video …