Summary of Mambaout: Do We Really Need Mamba For Vision?, by Weihao Yu et al.
MambaOut: Do We Really Need Mamba for Vision?
by Weihao Yu, Xinchao Wang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 performance of Mamba, a state space model (SSM) architecture with an RNN-like token mixer, on vision tasks. While Mamba has been applied to vision tasks, its performance is often underwhelming compared to convolutional and attention-based models. The authors hypothesize that Mamba’s long-sequence and autoregressive characteristics make it better suited for tasks such as detection and segmentation rather than image classification. To test this hypothesis, the authors construct a series of MambaOut models by stacking Mamba blocks without their core token mixer. Experimental results support the hypotheses, showing that Mamba is unnecessary for image classification but has potential for long-sequence visual tasks like detection and segmentation. The paper’s findings are supported by empirical evidence and provide insights into the applicability of Mamba to different vision tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new architecture for state space models (SSMs) that combines an RNN-like token mixer with the attention mechanism. Researchers tested it on vision tasks, but it didn’t perform as well as other models. They think this is because Mamba is better suited for longer sequences and autoregressive tasks, like detecting objects or segmenting images. To see if this was true, they made some changes to the model and tested it again. The results showed that Mamba isn’t needed for image classification, but it might be helpful for other vision tasks. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Image classification » Rnn » Token
