提出可适配多种骨干网络的抗畸变分割解码器，显著提升全景图像分割性能。
Deformable Mamba for Wide Field of View Segmentation
- 设计可适配CNN、Transformer和Mamba骨干的变形Mamba解码器
- 在360°数据集上提升2.5%精度，参数减少72%，计算量降97%
- 适合需要高效处理广角畸变图像的视觉任务，如全景分割
近年来，具有线性计算复杂度的Mamba架构成为克服传统Transformer二次复杂度问题的有前景替代方案。尽管现有工作主要关注将Mamba作为视觉编码器，但任务专用的Mamba解码器仍缺乏研究，尤其是在易受畸变影响的密集预测任务中。本文解决两个相互关联的问题：(1) 设计一种能无缝适配多种架构（如基于CNN、Transformer和Mamba的骨干网络）的Mamba解码器；(2) 解决缺乏畸变感知能力的解码器在处理广角图像（如180°鱼眼和360°全景）时性能下降的问题。我们提出变形Mamba解码器，一种兼具计算高效与自适应畸变感知能力的解码器。在五个广角分割基准上的综合实验验证了其有效性。值得注意的是，与广泛使用的解码头相比，该解码器在360° Stanford2D3D分割基准上提升2.5%性能，同时参数减少72%，浮点运算量降低97%。
原文摘要 · Abstract (English)
Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative to transformer architectures suffering from quadratic complexity. While existing works primarily focus on adapting Mamba as vision encoders, the critical role of task-specific Mamba decoders remains under-explored, particularly for distortion-prone dense prediction tasks. This paper addresses two interconnected challenges: (1) The design of a Mamba-based decoder that seamlessly adapts to various architectures (e.g., CNN-, Transformer-, and Mamba-based backbones), and (2) The performance degradation in decoders lacking distortion-aware capability when processing wide-FoV images (e.g., 180° fisheye and 360° panoramic settings). We propose the Deformable Mamba Decoder, an efficient distortion-aware decoder that integrates Mamba's computational efficiency with adaptive distortion awareness. Comprehensive experiments on five wide-FoV segmentation benchmarks validate its effectiveness. Notably, our decoder achieves a +2.5% performance improvement on the 360° Stanford2D3D segmentation benchmark while reducing 72% parameters and 97% FLOPs, as compared to the widely-used decoder heads.
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