Summary of V2xum-llm: Cross-modal Video Summarization with Temporal Prompt Instruction Tuning, by Hang Hua et al.
V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning
by Hang Hua, Yunlong Tang, Chenliang Xu, Jiebo Luo
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to multimodal video summarization is introduced in this paper, addressing the limitations of existing datasets and models. The Instruct-V2Xum dataset features 30,000 diverse YouTube videos with paired textual summaries that reference specific frame indexes, facilitating aligned video and textual summaries. A new framework, V2Xum-LLM, unifies different video summarization tasks into one large language model’s text decoder, achieving task-controllable video summarization with temporal prompts and task instructions. Experimental results show that V2Xum-LLaMA outperforms strong baseline models on multiple video summarization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to summarize videos is being developed! This paper helps make it easier to create short summaries of long videos by introducing a big dataset called Instruct-V2Xum. This dataset has 30,000 different YouTube videos with shorter descriptions that point to specific parts of the video. The goal is to make it easier for computers to understand what’s happening in a video and summarize it accurately. |
Keywords
» Artificial intelligence » Decoder » Large language model » Llama » Summarization
