AMPL:一个自适应的元提示学习器,用于几次拍摄的图像分类
Zhiping Wu1, Lian Huai2, Tong Liu2
1The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
概括
这项研究引入了适应式元提示式学习器 (AMPL) 用于少数拍摄图像的分类,提高了具有有限数据的新型类别的识别. 通过自适应地学习视觉提示和增强令牌意识,AMPL实现了最先进的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 短拍图像分类旨在使用最小的标记数据识别新类.
- 基于提示的学习在NLP中取得了成功,在图像分类中具有潜在但未被充分探索的应用.
- 使用提示符进行少数拍摄图像分类的现有方法通常是特定任务的,限制了适应性并增加了计算成本.
研究的目的:
- 引入一种新的框架,即自适应式元提示式学习器 (AMPL),用于有效的少数拍摄图像分类.
- 在适应性和计算效率方面解决特定任务快速学习的局限性.
- 为了在各种任务中提高少数镜头图像分类模型的稳定性和性能.
主要方法:
- 开发了自适应式元提示学习器 (AMPL) 框架,用于学习各种短暂任务的自适应式元视觉提示.
- 利用图像补丁功能来生成动态视觉提示以快速适应任务.
- 设计了一个令牌意识增强模块,通过相互令牌关系捕捉任务意识和视觉敏感的概念.
主要成果:
- 在七个几次拍摄的基准数据集上实现了新的最先进的分类性能.
- 证明了FC100数据集的显著改进,绝对准确度比手动调节的提示方法提高了3.88% (1次拍摄) 和7.96% (5次拍摄).
- 拟议的AMPL框架显示了在不同短时间学习场景中卓越的适应性和稳定性.
结论:
- 适应式元提示学习器 (AMPL) 框架通过自适应地学习元视觉提示,有效地增强了少数拍摄图像的分类.
- 代币意识增强模块通过利用代币之间的关系,有助于提高稳定性和性能.
- 在计算机视觉任务的基于提示的学习中,AMPL代表了显著的进步,提供了更好的准确性和效率.
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