Summary of Objectnlq @ Ego4d Episodic Memory Challenge 2024, by Yisen Feng et al.
ObjectNLQ @ Ego4D Episodic Memory Challenge 2024
by Yisen Feng, Haoyu Zhang, Yuquan Xie, 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 paper presents a novel approach, ObjectNLQ, for the Natural Language Query and Goal Step tracks of the Ego4D Episodic Memory Benchmark at CVPR 2024. The challenges require localizing actions within long video sequences using textual queries. To improve localization accuracy, ObjectNLQ incorporates an object branch to augment the video representation with detailed object information, enhancing grounding efficiency. The method achieves a mean R@1 of 23.15 in the Natural Language Queries Challenge and gains 33.00 in terms of the metric R@1, IoU=0.3 in the Goal Step Challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to improve action localization in long videos using text queries. They created a new method called ObjectNLQ that looks at both the video’s timeline and what’s happening in each frame. This helps find specific objects and actions better. The results are pretty good, ranking 2nd and 3rd in two challenges. |
Keywords
» Artificial intelligence » Grounding
