Summary of Grounded-videollm: Sharpening Fine-grained Temporal Grounding in Video Large Language Models, by Haibo Wang et al.
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
by Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
First submitted to arxiv on: 4 Oct 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 Grounded-VideoLLM model is designed to excel in fine-grained video understanding by incorporating an additional temporal stream and discrete temporal tokens enriched with specific time knowledge. The model is trained using a multi-stage training scheme, starting with simple video-captioning tasks and progressing to increasingly complex temporal grounding tasks. This approach enables the model to effectively perceive and reason over specific video moments. Grounded-VideoLLM outperforms existing Video-LLMs in fine-grained grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new type of language model that can understand videos at a more detailed level than before. The model is called Grounded-VideoLLM and it’s better at figuring out what’s happening in specific moments of a video. Right now, AI models are really good at understanding the overall story or theme of a video, but they struggle to understand the small details. The new model uses special techniques like adding extra information about time and using a special training process to help it learn these skills. This could lead to more advanced video analysis tools that can be used in all sorts of applications. |
Keywords
» Artificial intelligence » Grounding » Language model
