Summary of Semantic Prompting with Image-token For Continual Learning, by Jisu Han et al.
Semantic Prompting with Image-Token for Continual Learning
by Jisu Han, Jaemin Na, Wonjun Hwang
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes a novel approach to continual learning called I-Prompt, which leverages pre-trained models to learn new tasks without relying on rehearsal buffers. The approach is designed to eliminate task prediction and instead focuses on visual semantic information in image tokens. The method consists of two components: semantic prompt matching, which determines prompts based on token similarities, and image token-level prompting, which applies prompts directly to intermediate layers. I-Prompt achieves competitive performance on four benchmarks while reducing training time compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary I-Prompt is a new way for machines to learn new things without forgetting what they already know. It helps computers learn by using pictures and words to figure out what to do next. This approach works really well and is faster than other ways of doing it. It even does better in some situations where tasks are hard or different from each other. |
Keywords
* Artificial intelligence * Continual learning * Prompt * Prompting * Token
