Summary of Timerefine: Temporal Grounding with Time Refining Video Llm, by Xizi Wang et al.
TimeRefine: Temporal Grounding with Time Refining Video LLM
by Xizi Wang, Feng Cheng, Ziyang Wang, Huiyu Wang, Md Mohaiminul Islam, Lorenzo Torresani, Mohit Bansal, Gedas Bertasius, David Crandall
First submitted to arxiv on: 12 Dec 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 The proposed TimeRefine method addresses the challenge of accurately localizing temporal boundaries in videos given a textual prompt by reformulating the task as a refining process. This involves making rough predictions and then refining them through repeated offset predictions. Additionally, an auxiliary prediction head is incorporated to penalize deviations from ground truth, encouraging more accurate predictions. The TimeRefine method can be easily integrated into most LLM-based temporal grounding approaches. Experimental results show improvements of 3.6% and 5.0% mIoU on the ActivityNet and Charades-STA datasets, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TimeRefine is a new way to help machines understand videos better. When you tell a machine what’s happening in a video, it can be hard for them to know exactly when things are happening. TimeRefine makes this process easier by having the machine make some initial guesses and then refine those guesses until they’re really accurate. It also helps the machine stay on track by giving it a little “nudge” if its predictions aren’t quite right. This new method can be used with many other ways that machines are already learning to understand videos. |
Keywords
» Artificial intelligence » Grounding » Prompt
