Summary of Enhancing Long Video Understanding Via Hierarchical Event-based Memory, by Dingxin Cheng et al.
Enhancing Long Video Understanding via Hierarchical Event-Based Memory
by Dingxin Cheng, Mingda Li, Jingyu Liu, Yongxin Guo, Bin Jiang, Qingbin Liu, Xi Chen, Bo Zhao
First submitted to arxiv on: 10 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 In this paper, researchers propose a new approach to improve video understanding systems by integrating visual foundation models into large language models (LLMs). The existing methods compress diverse semantic information within long videos, which can lead to information redundancy and obscure the semantics of key events. To address this issue, they design a Hierarchical Event-based Memory-enhanced LLM (HEM-LLM) that segments multiple events within long videos and performs individual memory modeling for each event to establish intra-event contextual connections. This approach reduces information redundancy and enhances long-term inter-event dependencies in videos. The authors conduct extensive experiments on various video understanding tasks and achieve state-of-the-art performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand long videos better. Right now, most video understanding systems take all the information from a long video and feed it into a large language model (LLM) for analysis. This works well for short videos, but for long ones, it can lead to too much information and hide important details. The researchers came up with an idea called HEM-LLM that helps solve this problem. They divide the video into smaller events, analyze each one separately, and then connect them together to understand the whole video better. |
Keywords
» Artificial intelligence » Large language model » Semantics
