V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning

arXiv:2404.12353v1 Announce Type: new
Abstract: Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is their limited amount of source videos, which hampers the effective fine-tuning of advanced large vision-language models (VLMs). Additionally, most existing datasets are created for video-to-video summarization, overlooking the contemporary need for multimodal video content summarization. Recent efforts have been made to expand from unimodal to multimodal video summarization, categorizing the …

abstract advanced arxiv create cs.ai cs.cv datasets fine-tuning language language models llm modal prompt summarization temporal type video videos vision vision-language vision-language models vlms