Summary of From Seconds to Hours: Reviewing Multimodal Large Language Models on Comprehensive Long Video Understanding, by Heqing Zou et al.
From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding
by Heqing Zou, Tianze Luo, Guiyang Xie, Victor, Zhang, Fengmao Lv, Guangcong Wang, Junyang Chen, Zhuochen Wang, Hansheng Zhang, Huaijian Zhang
First submitted to arxiv on: 27 Sep 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 Recent advancements in MultiModal Large Language Models (MM-LLMs) have shown promising results in visual understanding tasks, leveraging their ability to comprehend and generate human-like text for visual reasoning. Our paper focuses on the unique challenges posed by long video understanding compared to static image and short video understanding. Unlike static images, short videos encompass sequential frames with both spatial and within-event temporal information, while long videos consist of multiple events with between-event and long-term temporal information. We review the advancements of MM-LLMs from image understanding to long video understanding, highlighting differences among various visual understanding tasks and challenges in long video understanding, including fine-grained spatiotemporal details, dynamic events, and long-term dependencies. We also provide a detailed summary of MM-LLM model design and training methodologies for understanding long videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a movie or a TV show just by looking at individual frames. It’s hard, right? That’s because each frame has its own story, and they all work together to tell the whole story. This is similar to how our brains process visual information. Now, imagine we have machines that can do this too! These machines are called MultiModal Large Language Models (MM-LLMs). Our paper looks at how MM-LLMs can understand long videos, which are even harder to understand than movies or TV shows. We review what’s been discovered so far and talk about the challenges of understanding these complex videos. |
Keywords
» Artificial intelligence » Spatiotemporal
