Summary of Modelgrow: Continual Text-to-video Pre-training with Model Expansion and Language Understanding Enhancement, by Zhefan Rao et al.
ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement
by Zhefan Rao, Liya Ji, Yazhou Xing, Runtao Liu, Zhaoyang Liu, Jiaxin Xie, Ziqiao Peng, Yingqing He, Qifeng Chen
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the concept of continual general pre-training for text-to-video (T2V) models, which enables them to “grow” their abilities based on a pre-trained foundation. The authors propose ModelGrow, a novel approach that breaks down this task into increasing model capacity and improving semantic understanding. To achieve this, they introduce several techniques to expand the model size, allowing it to store new knowledge and improve generation performance. Additionally, they leverage large language models as advanced text encoders to enhance language comprehension and guide generation results according to detailed prompts. The proposed method enables the model to achieve better semantic alignment, particularly in response to complex user prompts. Extensive experiments demonstrate the effectiveness of ModelGrow across various metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers that can turn words into videos smarter. Right now, these computers are very expensive and not very good at turning words into videos. The authors want to find a way to make them better without having to spend so much money or time. They came up with an idea called ModelGrow, which helps the computer learn new things based on what it already knows. They tested this idea and found that it works really well. |
Keywords
* Artificial intelligence * Alignment
