Summary of On the Low-shot Transferability Of [v]-mamba, by Diganta Misra et al.
On the low-shot transferability of [V]-Mamba
by Diganta Misra, Jay Gala, Antonio Orvieto
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the transfer learning potential of [V]-Mamba, a variant of Vision Transformers (ViTs), in few-shot learning scenarios. It compares [V]-Mamba’s performance with ViTs across different data budgets and efficient transfer methods. The study reveals three key insights: [V]-Mamba outperforms or matches ViTs when using linear probing (LP) for transfer, but underperforms or matches ViTs when employing visual prompting (VP). Additionally, the performance gap between LP and VP is positively correlated with the scale of the [V]-Mamba model. This preliminary analysis sets the stage for further research on [V]-Mamba’s capabilities and distinctions from ViTs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary [V]-Mamba is a new way to train neural networks that can learn quickly even when given very little data. The paper compares it to another popular approach called Vision Transformers (ViTs) and finds that [V]-Mamba is better at some tasks, but not others. When given a few examples of what to do, [V]-Mamba can figure things out just as well as ViTs. However, when shown pictures with specific objects or actions, ViTs tend to perform better. The study also found that the size of the [V]-Mamba model affects how well it performs in certain situations. |
Keywords
* Artificial intelligence * Few shot * Prompting * Transfer learning
