Summary of Videgothink: Assessing Egocentric Video Understanding Capabilities For Embodied Ai, by Sijie Cheng et al.
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
by Sijie Cheng, Kechen Fang, Yangyang Yu, Sicheng Zhou, Bohao Li, Ye Tian, Tingguang Li, Lei Han, Yang Liu
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper introduces VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities of Multi-modal Large Language Models (MLLMs). The benchmark consists of four interrelated tasks: video question-answering, hierarchy planning, visual grounding, and reward modeling. To minimize manual annotation costs, the authors develop an automatic data generation pipeline based on the Ego4D dataset, leveraging GPT-4o’s prior knowledge and multimodal capabilities. Experimental results indicate that all MLLMs, including GPT-4o, perform poorly across all tasks related to egocentric video understanding. These findings suggest that foundation models still require significant advancements to be effectively applied to first-person scenarios in Embodied AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well artificial intelligence can understand videos from a person’s point of view. The authors made four challenges: answering questions about what’s happening in the video, planning what to do next, finding specific things in the video, and deciding if something is good or not. They also developed a way to automatically create more data for testing, using information from a previous dataset and a language model called GPT-4o. The tests showed that most of these models are not very good at understanding videos this way. This means that these artificial intelligence models need to get better before they can be used in real-life situations where people interact with the environment. |
Keywords
» Artificial intelligence » Gpt » Grounding » Language model » Multi modal » Question answering
