# A Separable Self-attention Inspired by the State Space Model for Computer Vision Source: [A Separable Self-attention Inspired by the State Space Model for Computer Vision]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:16.666Z Authors: Juntao Zhang, Shaogeng Liu, Kun Bian, You Zhou, Pei Zhang, Jianning Liu, Jun Zhou, Bingyan Liu Published: 2025-01-03T15:23:36.000Z ## Abstract Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods still demonstrate good performance in tasks such as image classification and object detection. Recent studies have shown that there is a rich theoretical connection between state space models and attention variants. We propose a novel separable self attention method, for the first time introducing some excellent design concepts of Mamba into separable self-attention. To ensure a fair comparison with ViMs, we introduce VMINet, a simple yet powerful prototype architecture, constructed solely by stacking our novel attention modules with the most basic down-sampling layers. Notably, VMINet differs significantly from the conventional Transformer architecture. Our experiments demonstrate that VMINet has achieved competitive results on image classification and high-resolution dense prediction tasks.Code is available at: https://github.com/yws-wxs/VMINet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()