Summary of Lspt: Long-term Spatial Prompt Tuning For Visual Representation Learning, by Shentong Mo et al.
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning
by Shentong Mo, Yansen Wang, Xufang Luo, Dongsheng Li
First submitted to arxiv on: 27 Feb 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 The paper introduces Long-term Spatial Prompt Tuning (LSPT), a novel approach to adapting pre-trained Vision Transformers (ViTs) for downstream visual tasks. LSPT uses long-range gated prompts and patch tokens to leverage the model’s previous blocks, improving its ability to learn from earlier experiences and distinguishing between visual categories. The proposed method is validated through experiments on 5 FGVC and 19 VTAB-1K benchmarks, demonstrating superior performance in visual prompt tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LSPT helps ViTs remember what they’ve learned before, using special tokens that store information from earlier blocks. This makes the model better at recognizing different things. The authors tested LSPT on many pictures and found it does a great job! |
Keywords
* Artificial intelligence * Prompt
