马巴-Reg:愿景马巴也需要注册
Feng Wang1, Jiahao Wang1, Sucheng Ren1
1Johns Hopkins University.
概括
本研究介绍了Mamba®,这是一个改进的Vision Mamba架构,可以减少特征地图中的文物. 玛巴®提高了图像分析性能和可扩展性,优于现有的模型.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习架构 深度学习架构
背景情况:
- 像Vision Transformers一样,Vision Mamba在功能地图中展示了文物,特别是在背景区域的高标准令牌.
- 这些文物在Vision Mamba中更加严重,甚至出现在小模型中,并在背景区域广泛激活.
研究的目的:
- 为了减轻Vision Mamba中的功能地图文物.
- 通过修改注册表令牌集成,为Mamba的单向推理引入一个改进的架构,Mamba®.
主要方法:
- 将注册令牌引入Vision Mamba,这是一个已知的工件减轻解决方案.
- 实施了两个关键的修改:在整个令牌序列中均插入注册表和回收注册表用于预测.
- 开发了采用这些修改的Mamba®架构.
主要成果:
- 与香草Vision Mamba相比,Mamba®展示了更清洁的特征地图,更好地关注语义上有意义的区域.
- 在ImageNet上实现了更高的精度 (Mamba®-B的83.0%,Vim-B的81.8%).
- 成功扩展到341M参数模型,实现了竞争力的准确度 (83.6%和84.5%不同的输入大小).
结论:
- Mamba®有效地减少了文物,并提高了Vision Mamba中的功能地图质量.
- 拟议的修改带来了更高的性能和更好的可扩展性.
- 曼巴®在下游任务中表现出强大的有效性,例如语义细分.
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