With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Long-form video understanding has been a challenging task due to the high redundancy in
video data and the abundance of query-irrelevant information. To tackle this challenge, we …

What makes good representations for video understanding, such as anticipating future
activities, or answering video-conditioned questions? While earlier approaches focus on …

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 …

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

Abstract Video Large Language Models (Video-LLMs) are flourishing and has advanced
many video-language tasks. As a golden testbed, Video Question Answering (VideoQA) …

In the video-language domain, recent works in leveraging zero-shot Large Language Model-
based reasoning for video understanding have become competitive challengers to previous …

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 …

Long-form videos that span across wide temporal intervals are highly information redundant
and contain multiple distinct events or entities that are often loosely related. Therefore, when …

Verbalization of robot experience, ie, summarization of and question answering about a
robot's past, is a crucial ability for improving human-robot interaction. Previous works …