Summary of Cvpt: Cross-attention Help Visual Prompt Tuning Adapt Visual Task, by Lingyun Huang et al.
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task
by Lingyun Huang, Jianxu Mao, Yaonan Wang, Junfei Yi, Ziming Tao
First submitted to arxiv on: 27 Aug 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 Cross Visual Prompt Tuning (CVPT) method refines the widely-used Visual Prompt Tuning (VPT) approach for efficient and effective visual fine-tuning of large-scale pre-trained models. Building upon adapter-based and prompt-based PEFT methods, CVPT calculates cross-attention between prompt tokens and embedded tokens to compute semantic relationships and adapt models for specific tasks. The introduction of a weight-sharing mechanism initializes parameters without massive learnable parameters, enhancing the representative capability of cross-attention. Comprehensive testing across 25 datasets demonstrates significant performance improvements over VPT, rivaling adapter-based methods in efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CVPT is a new way to fine-tune big models for visual tasks. Normally, these models are trained on lots of data and then adjusted for specific jobs like image classification or object detection. CVPT makes this process more efficient by understanding how the words in the prompts relate to the images being processed. This means that the model can learn from a smaller amount of training data and still do well. The creators tested CVPT on many datasets and found that it works better than an older method called VPT, which is also prompt-based. In some cases, CVPT even outperformed more complex methods that use adapters. |
Keywords
» Artificial intelligence » Cross attention » Fine tuning » Image classification » Object detection » Prompt
