Summary of Temporal Grounding Of Activities Using Multimodal Large Language Models, by Young Chol Song
Temporal Grounding of Activities using Multimodal Large Language Models
by Young Chol Song
First submitted to arxiv on: 30 May 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 This paper investigates the application of multimodal large language models (LLMs) for temporal grounding of activities in videos. The authors evaluate a two-stage approach combining image-based and text-based LLMs for localizing specific time intervals within larger events. They demonstrate that this method outperforms existing video-based LLMs on the Charades-STA dataset. Additionally, they explore the impact of instruction-tuning on a smaller multimodal LLM, showing its ability to process action queries can be refined to improve performance in temporal activity localization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re watching a video and you need to know what specific actions happened at certain times. This paper helps with that by using special computer models called large language models (LLMs). They tested different types of LLMs and found that combining image-based and text-based models is better than just using one type alone. They also showed how fine-tuning these models can make them even more accurate in identifying specific times when certain actions happen. This has big implications for understanding videos and could be used to help machines learn from video data. |
Keywords
» Artificial intelligence » Fine tuning » Grounding » Instruction tuning
