Summary of Nemesis: Normalizing the Soft-prompt Vectors Of Vision-language Models, by Shuai Fu et al.
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
by Shuai Fu, Xiequn Wang, Qiushi Huang, Yu Zhang
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 research paper investigates the role of norms in soft-prompt tuning for large-scale vision-language models (VLMs). The authors aim to answer whether normalizing soft prompts improves performance. They identify a phenomenon called the Low-Norm Effect, where reducing norms occasionally enhances VLM performance while increasing them often degrades it. To harness this effect, they propose Nemesis, a novel method for normalizing soft-prompt vectors in VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main contribution is understanding the impact of norms on soft prompts and proposing Nemesis to normalize them. This work provides valuable insights for future research in soft-prompt tuning. |
Keywords
» Artificial intelligence » Prompt
