Attention Is not Everything: Efficient Alternatives for Vision
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910146158919680 |
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| author | Kazi, Nur Mohammad Khaled, Ibteshum Galib, Md. Luthful Hasan Shihab, Ali Faruk Islam, Md. Rakibul |
| author_facet | Kazi, Nur Mohammad Khaled, Ibteshum Galib, Md. Luthful Hasan Shihab, Ali Faruk Islam, Md. Rakibul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17439 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Attention Is not Everything: Efficient Alternatives for Vision Kazi, Nur Mohammad Khaled, Ibteshum Galib, Md. Luthful Hasan Shihab, Ali Faruk Islam, Md. Rakibul Computer Vision and Pattern Recognition 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. |
| title | Attention Is not Everything: Efficient Alternatives for Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.17439 |