MambaOut: Do We Really Need Mamba for Vision?

🌈 Abstract

The paper investigates the necessity of using the Mamba architecture, which incorporates a state space model (SSM) token mixer, for various computer vision tasks. The authors analyze the characteristics that make Mamba well-suited for certain tasks and examine whether common vision tasks exhibit those characteristics. Based on this analysis, the authors propose two hypotheses:

1. SSM is not necessary for image classification on ImageNet, as this task does not require long sequences or autoregressive token mixing.
2. SSM may be potentially beneficial for object detection & instance segmentation and semantic segmentation, as these tasks involve long sequences, even though they are not autoregressive.

To validate these hypotheses, the authors develop a series of models called "MambaOut" that use the Gated CNN block without the SSM component. The experimental results support the authors' hypotheses, showing that MambaOut outperforms visual Mamba models on ImageNet classification, but does not match the performance of state-of-the-art visual Mamba models on detection and segmentation tasks.

🙋 Q&A

[01] Conceptual discussion

1. What characteristics make Mamba well-suited for certain tasks? The authors identify two key characteristics that make Mamba well-suited for certain tasks:

• Characteristic 1: The task involves processing long sequences.
• Characteristic 2: The task requires causal token mixing mode.

2. How do the authors analyze whether visual recognition tasks exhibit these characteristics? For long sequences, the authors use a simple metric based on the computational complexity of Transformer blocks to determine if a task involves long sequences. They find that image classification on ImageNet does not qualify as a long-sequence task, while object detection & instance segmentation on COCO and semantic segmentation on ADE20K can be considered long-sequence tasks.

For the causal token mixing mode, the authors explain that visual recognition tasks are categorized as understanding tasks, where the model can see the entire image at once. Imposing additional constraints on token mixing, as in the causal mode, can potentially degrade model performance, as shown in their experiments with ViT.

3. What are the authors' hypotheses regarding the necessity of Mamba for vision tasks? Based on the analysis, the authors propose two hypotheses:

• Hypothesis 1: SSM is not necessary for image classification on ImageNet, as this task does not meet Characteristic 1 or Characteristic 2.
• Hypothesis 2: It is still worthwhile to further explore the potential of SSM for visual detection and segmentation tasks, as these tasks align with Characteristic 2, despite not fulfilling Characteristic 1.

[02] Experimental verification

1. How do the authors design the MambaOut models to validate their hypotheses? The authors develop a series of MambaOut models based on the Gated CNN block, which is similar to the Mamba block but without the SSM component. This allows them to assess the necessity of the SSM for different visual recognition tasks.

2. What are the key findings from the experiments on ImageNet classification? The experiments show that the simpler MambaOut models consistently outperform the visual Mamba models on ImageNet classification, supporting the authors' Hypothesis 1 that SSM is unnecessary for this task.

3. How do the results on detection and segmentation tasks compare between MambaOut and visual Mamba models? While MambaOut can outperform some visual Mamba models on detection and segmentation tasks, it still falls short of matching the performance of state-of-the-art visual Mamba models. This underscores the potential benefits of incorporating SSM for long-sequence visual tasks, as stated in the authors' Hypothesis 2.

4. What is the significance of the MambaOut models in the context of future research on visual Mamba models? The authors suggest that MambaOut, due to its Occam's razor nature, may serve as a natural baseline for future research on visual Mamba models, as it effectively surpasses existing visual Mamba models on ImageNet classification.