Summary of Hcqa @ Ego4d Egoschema Challenge 2024, by Haoyu Zhang et al.
HCQA @ Ego4D EgoSchema Challenge 2024
by Haoyu Zhang, Yuquan Xie, Yisen Feng, Zaijing Li, Meng Liu, Liqiang Nie
First submitted to arxiv on: 22 Jun 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 proposed Hierarchical Comprehension scheme for egocentric video Question Answering, named HCQA, is a novel approach that combines the power of egocentric captioning and question reasoning models. The scheme consists of three stages: Fine-grained Caption Generation, Context-driven Summarization, and Inference-guided Answering. This hierarchical information is then used to reason and answer given questions, achieving 75% accuracy on the EgoSchema blind test set. HCQA utilizes powerful egocentric captioning models and question reasoning models, making it a top-performing solution for the Ego4D EgoSchema Challenge in CVPR 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to understand videos by combining two important skills: recognizing what’s happening in the video and answering questions about it. The approach is called Hierarchical Comprehension, or HCQA for short. It works by first breaking down the video into small parts and then summarizing those parts. Then, it uses this information to answer questions about the video. This method was tested on a big set of videos and questions, and it got 75% of the answers correct. |
Keywords
» Artificial intelligence » Inference » Question answering » Summarization
