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arXiv · 2604.17439

Attention Is not Everything: Efficient Alternatives for Vision

Nur Mohammad Kazi·Ibteshum Khaled·Md. Luthful Hasan Galib·Ali Faruk Shihab·Md. Rakibul Islam
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Abstract

Recently computer vision has seen advancements mainly thanks to Transformer-based models. However many non-Transformer methods are still doing well being a direct competition of Transformer-based models. This review tries to present a comprehensive taxonomy of such methods and organize these methods into categories like convolution-based models, MLP-based models, state-space-based and more. These methods are looked at in terms of how efficient they are, how well they scale, how easy they are to understand and how robust they are. A total of 40 papers were chosen for this study. The goal is to give a view of non-Transformer methods and find out what challenges and opportunities exist for future computer vision research.

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Nur Mohammad Kazi, Ibteshum Khaled, Md. Luthful Hasan Galib, Ali Faruk Shihab, Md. Rakibul Islam. 2026-04-19. Attention Is not Everything: Efficient Alternatives for Vision. https://arxiv.org/abs/2604.17439

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