The strength of modern large-scale neural networks lies in their ability to
efficiently adapt to new tasks with few examples. Although extensive research
has investigated the transferability of Vision Transformers (ViTs) to various
downstream tasks under diverse constraints, this study shifts focus to explore
the transfer learning potential of [V]-Mamba. We compare its performance with
ViTs across different few-shot data budgets and efficient transfer methods. Our
analysis yields three key insights into [V]-Mamba's few-shot transfer
performance: (a) [V]-Mamba demonstrates superior or equivalent few-shot
learning capabilities compared to ViTs when utilizing linear probing (LP) for
transfer, (b) Conversely, [V]-Mamba exhibits weaker or similar few-shot
learning performance compared to ViTs when employing visual prompting (VP) as
the transfer method, and (c) We observe a weak positive correlation between the
performance gap in transfer via LP and VP and the scale of the [V]-Mamba model.
This preliminary analysis lays the foundation for more comprehensive studies
aimed at furthering our understanding of the capabilities of [V]-Mamba variants
and their distinctions from ViTs.

本文研究了大规模神经网络在少样本迁移学习中的能力，通过对 [V]-Mamba 模型与 ViTs 模型在不同少样本数据预算和高效迁移方法下的性能比较，发现 [V]-Mamba 在线性探测（LP）迁移方案下表现优于或等同于 ViTs，而在视觉提示（VP）迁移方法下表现较弱或类似于 ViTs，同时 [V]-Mamba 模型的规模与迁移方案之间存在弱正相关性。这项初步分析为进一步研究 [V]-Mamba 变体和 ViTs 之间的差异，以及它们的能力提供了基础。