A Survey on Mamba Architecture for Vision Applications

• URL: http://arxiv.org/abs/2502.07161v1
• Date: Tue, 11 Feb 2025 00:59:30 GMT
• Title: A Survey on Mamba Architecture for Vision Applications
• Authors: Fady Ibrahim, Guangjun Liu, Guanghui Wang,
• Abstract summary: Mamba architecture addresses scalability challenges in visual tasks.<n>Vision Mamba and VideoMamba introduce bidirectional scanning, selective mechanisms, andtemporal processing to enhance image and video understanding.<n>These advancements position Mamba as a promising architecture in computer vision research and applications.
• Score: 7.216568558372857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have become foundational for visual tasks such as object detection, semantic segmentation, and video understanding, but their quadratic complexity in attention mechanisms presents scalability challenges. To address these limitations, the Mamba architecture utilizes state-space models (SSMs) for linear scalability, efficient processing, and improved contextual awareness. This paper investigates Mamba architecture for visual domain applications and its recent advancements, including Vision Mamba (ViM) and VideoMamba, which introduce bidirectional scanning, selective scanning mechanisms, and spatiotemporal processing to enhance image and video understanding. Architectural innovations like position embeddings, cross-scan modules, and hierarchical designs further optimize the Mamba framework for global and local feature extraction. These advancements position Mamba as a promising architecture in computer vision research and applications.

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