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Separators in Enhancing Autoregressive Pretraining for Vision Mamba
🤖AI Summary
Researchers introduce STAR, a new autoregressive pretraining method for Vision Mamba that uses separators to quadruple input sequence length while maintaining image dimensions. The STAR-B model achieved 83.5% accuracy on ImageNet-1k, demonstrating improved performance through better utilization of long-range dependencies in computer vision tasks.
Key Takeaways
- →Vision Mamba's causal mechanism makes it well-suited for autoregressive pretraining but current methods are limited to short sequences.
- →STAR introduces identical separators before each image to demarcate different images and extend sequence length by 4x.
- →The method preserves original dataset image dimensions while significantly increasing input sequence capacity.
- →STAR-B achieved 83.5% accuracy on ImageNet-1k, showing competitive performance in Vision Mamba models.
- →The approach demonstrates potential for enhancing vision model performance through improved long-range dependency modeling.
#vision-mamba#autoregressive#pretraining#computer-vision#sequence-modeling#imagenet#deep-learning#state-space-models
Read Original →via arXiv – CS AI
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