This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for
video understanding, that capably understands arbitrary-length video with a constant …

We present LLoVi, a language-based framework for long-range video question-answering
(LVQA). Unlike prior long-range video understanding methods, which are often costly and …

Long-form video understanding represents a significant challenge within computer vision,
demanding a model capable of reasoning over long multi-modal sequences. Motivated by …

Scaling deep learning to massive, diverse internet data has yielded remarkably general
capabilities in visual and natural language understanding and generation. However, data …

Abstract We present Any-Modality Augmented Language Model (AnyMAL), a unified model
that reasons over diverse input modality signals (ie text, image, video, audio, IMU motion …

Language has become a prominent modality in computer vision with the rise of LLMs.
Despite supporting long context-lengths, their effectiveness in handling long-term …

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 …

Recent advancements in image understanding have benefited from the extensive use of
web image-text pairs. However, video understanding remains a challenge despite the …

Recent advancements in large-scale video-language models have shown significant
potential for real-time planning and detailed interactions. However, their high computational …

Existing methods for long video understanding primarily focus on videos only lasting tens of
seconds, with limited exploration of techniques for handling longer videos. The increased …