Summary of Eagle: Egocentric Aggregated Language-video Engine, by Jing Bi et al.
EAGLE: Egocentric AGgregated Language-video Engine
by Jing Bi, Yunlong Tang, Luchuan Song, Ali Vosoughi, Nguyen Nguyen, Chenliang Xu
First submitted to arxiv on: 26 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 The EAGLE (Egocentric AGgregated Language-video Engine) model is a unified framework that integrates various egocentric video understanding tasks. It’s designed to effectively capture both spatial and temporal information, and it outperforms existing models in balancing task-specific understanding with holistic video interpretation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces the EAGLE model and the EAGLE-400K dataset, which is a large-scale instruction-tuning dataset tailored for egocentric video. The dataset has 400K diverse samples that enhance various tasks from activity recognition to procedure knowledge learning. The EAGLE model is a strong video multimodal large language model (MLLM) that can be used in real-world scenarios. |
Keywords
» Artificial intelligence » Activity recognition » Instruction tuning » Large language model
