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Investigating [V]-Mamba's Few-Shot Transfer Performance Compared to ViTs

Core Concepts
[V]-Mamba shows comparable or superior few-shot learning capabilities to ViTs with linear probing but weaker performance with visual prompting.
The study explores the transfer learning potential of [V]-Mamba compared to Vision Transformers (ViTs) in low-shot scenarios. It analyzes the performance using linear probing (LP) and visual prompting (VP) methods. Results show that [V]-Mamba outperforms ViTs with LP but lags behind with VP. A weak positive correlation is observed between model scale and transfer method performance gap. The research lays the groundwork for further studies on [V]-Mamba variants.
VSSM-T: 60.45% test accuracy from ImageNet-1k dataset. VSSM-S: 52.43% test accuracy from ImageNet-1k dataset.
"Linear probing shows [V]-Mamba as a strong few-shot learner compared to ViTs." "[V]-Mamba's performance is weaker when employing visual prompting for few-shot transfer."

Key Insights Distilled From

by Diganta Misr... at 03-19-2024
On the low-shot transferability of [V]-Mamba

Deeper Inquiries

How can the findings of this study impact the development of future deep learning models?

The findings of this study shed light on the transferability and performance differences between Vision Transformers (ViTs) and [V]-Mamba models in few-shot learning scenarios. This insight can influence the development of future deep learning models by guiding researchers towards more effective transfer methods based on empirical evidence. Understanding which model performs better under specific conditions, such as linear probing or visual prompting, can help in designing more efficient and accurate models for various downstream tasks.

What factors might contribute to the weaker performance of [V]-Mamba with visual prompting?

Several factors could contribute to the weaker performance of [V]-Mamba with visual prompting compared to ViTs. One possible factor is the architecture and design differences between ViTs and Mamba-based models, leading to variations in how they adapt to different transfer methods. Additionally, it could be related to how well each model handles input transformations and output mappings required for visual prompting. The complexity of integrating these components effectively into a pre-trained model like Mamba may also play a role in its weaker performance with this method.

How can state-space models like Mamba be applied beyond vision tasks, considering their efficiency in sequence modeling?

State-space models like Mamba offer advantages in sequence modeling due to their efficient recurrent mechanisms that are particularly appealing for long sequences. Beyond vision tasks, these models can be applied in natural language processing (NLP) for tasks such as text generation, sentiment analysis, or machine translation. Their ability to capture dependencies over long ranges makes them suitable for handling sequential data effectively. Furthermore, state-space models could find applications in time-series forecasting, speech recognition, bioinformatics data analysis, and other domains where understanding temporal relationships is crucial.