Vision Mamba BEATS Transformers!!!

From the Paper Abstract:


Recently the state space models (SSMs) with efficient
hardware-aware designs, i.e., Mamba, have shown great
potential for long sequence modeling. Building efficient and
generic vision backbones purely upon SSMs is an appealing
direction. However, representing visual data is challenging
for SSMs due to the position-sensitivity of visual data and
the requirement of global context for visual understanding.
In this paper, we show that the reliance of visual representation learning on self-attention is not necessary and propose …

abstract building data designs hardware mamba modeling paper sensitivity space state transformers vision visual visual data