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MambaOut: Do We Really Need Mamba for Vision? - GitHub
The Mamba block extends the Gated CNN with an additional state space model (SSM) As will be conceptually discussed in Section 3, SSM is not necessary for image classification on ImageNet
【即插即用模块】CVPR 2025 | MambaOut：只用卷积不用Mamba竟然比Mamba还好？！
为了实证验证我们的假设，我们通过在移除核心令牌混合器 SSM 的同时堆叠 Mamba 块来构建一系列名为 MambaOut 的模型。仅用Gated CNN Block而不使用SSM，能实现比视觉Mamba更优的准确率，更少的计算成本：
[2405. 07992] MambaOut: Do We Really Need Mamba for Vision? - arXiv. org
In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics
MambaOut: Do We Really Need Mamba for Vision? - CVF Open Access
In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive character-istics
论文阅读 | CVPR | MambaOut：视觉任务真的需要 Mamba 吗？-CSDN博客
图像分类任务并不具备这两个特性（长序列和自回归特性），因此假设 Mamba对于该任务并非必需；检测和分割任务虽然不是自回归的，但具有长序列特性；构建名为 MambaOut 的模型，这些模型通过堆叠 Mamba块并去除其核心令牌混合器 SSM 得到。
【CVPR 2025】即插即用MambaOut，分类、检测、分割表现巨大潜力！_mamba out论文-CSDN博客
文章浏览阅读5 6k次，点赞38次，收藏36次。本文探讨了Mamba架构在视觉任务中的必要性，通过构建不包含状态空间模型（SSM）的MambaOut模型，验证了SSM对于图像分类任务并非必需，但对于检测和分割任务可能有益。 _mamba out论文
MambaOut: Do We Really Need Mamba for Vision?总结分析
这就引出了一个引人注目的研究问题: Do we really need Mamba for Vision? 在本文中，研究了曼巴的本质，并从概念上总结了曼巴非常适合具有两个关键特征的任务: 长序列和自回归，因为SSM固有的RNN机制 (见图2和图3的解释)。
Paper page - MambaOut: Do We Really Need Mamba for Vision?
A Survey on Vision Mamba: Models, Applications and Challenges (2024) Please give a thumbs up to this comment if you found it helpful! If you want recommendations for any Paper on Hugging Face checkout this Space You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @ librarian-bot recommend