arxiv preprint - Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

In this episode, we discuss Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model by Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang. The paper introduces a new vision backbone called Vim, which leverages bidirectional Mamba blocks for efficient and effective visual representation learning, sidestepping the need for self-attention mechanisms. Vim incorporates position embeddings for handling the position-sensitivity of visual data and uses state space models to handle global context, leading to better performance on various tasks such as ImageNet classification and COCO object detection, while being more computationally and memory efficient than existing models like DeiT. Tests show that Vim is significantly faster and more memory-efficient, making it a promising candidate for advanced vision backbone algorithms, especially for high-resolution image processing.