Understanding long real-world videos requires modeling of long-range visual
dependencies. To this end we explore video-first architectures building on the common …

We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-
the-art performance in action recognition, video-text tasks, and video-centric dialogue. Our …

Most transformer-based video encoders are limited to short temporal contexts due to their
quadratic complexity. While various attempts have been made to extend this context, this …

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 …

We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively
evaluate large vision-language models in open-ended video question answering. The …

While existing video benchmarks largely consider specialized downstream tasks like
retrieval or question-answering (QA) contemporary multimodal AI systems must be capable …

With the rapid advancement of multimodal large language models (MLLMs), their evaluation
has become increasingly comprehensive. However, understanding long multimodal content …

Recent progress in multimodal large language models has markedly enhanced the
understanding of short videos (typically under one minute), and several evaluation datasets …

Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of"
world models"--interpreting and reasoning about complex real-world dynamics. To assess …

The recent developments in Large Multi-modal Video Models (Video-LMMs) have
significantly enhanced our ability to interpret and analyze video data. Despite their …