Summary of Mm-ego: Towards Building Egocentric Multimodal Llms, by Hanrong Ye et al.
MM-Ego: Towards Building Egocentric Multimodal LLMs
by Hanrong Ye, Haotian Zhang, Erik Daxberger, Lin Chen, Zongyu Lin, Yanghao Li, Bowen Zhang, Haoxuan You, Dan Xu, Zhe Gan, Jiasen Lu, Yinfei Yang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This research aims to develop a comprehensive foundation model for understanding egocentric videos. To achieve this goal, three main components are explored: generating high-quality QA data for egocentric videos, creating an egocentric QA benchmark, and proposing a specialized multimodal architecture featuring a novel “Memory Pointer Prompting” mechanism. The generated dataset consists of 7 million QA samples for videos ranging from 30 seconds to one hour long, which is the largest available dataset for egocentric video understanding. A new de-biasing evaluation method is introduced to mitigate language bias in model evaluations. The proposed architecture includes a global glimpse step to gain an overview of the entire video and identify key visual information, followed by a fallback step that uses this information to generate responses. This enables the model to effectively comprehend extended video content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a tool for understanding videos taken from someone’s point of view (like a selfie). To make it work, they had to do three things: create lots of questions and answers about these types of videos, develop a way to test how well models can answer these questions, and design a special model that can understand long videos. They made a huge dataset with 7 million questions and answers, which is the biggest one for this type of video understanding. They also came up with a new way to make sure their tests aren’t biased towards certain types of language. The model they created is called MM-Ego, and it’s really good at understanding these types of videos. |
Keywords
» Artificial intelligence » Prompting
