Summary of Infusing Environmental Captions For Long-form Video Language Grounding, by Hyogun Lee et al.
Infusing Environmental Captions for Long-Form Video Language Grounding
by Hyogun Lee, Soyeon Hong, Mujeen Sung, Jinwoo Choi
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed EI-VLG method tackles long-form video-language grounding tasks by leveraging richer textual information from Multi-modal Large Language Models (MLLMs) as a proxy for human experiences, effectively excluding irrelevant frames. The approach addresses the limitations of existing methods, which often rely on superficial cues learned from small-scale datasets and can fall into irrelevance even when within incorrect frames. To validate its effectiveness, the proposed method is tested extensively on a challenging EgoNLQ benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find a specific moment in a very long video that answers a question you asked. Humans are great at doing this, but current machines aren’t as good. They often get distracted by things they see in the video and can’t ignore irrelevant parts. The researchers created a new way for machines to do this task called EI-VLG. It uses extra information from large language models to help machines focus on the right moments in the video. This approach was tested on a difficult benchmark and showed it could be very effective. |
Keywords
* Artificial intelligence * Grounding * Multi modal
