用于宽视场分割的可变形Mamba
Deformable Mamba for Wide Field of View Segmentation
AI总结：
本文提出可变形Mamba解码器，解决宽视场图像分割中解码器缺乏畸变感知能力的问题，在多个基准上验证其有效性，并在Stanford2D3D上以更少参数和FLOPs实现性能提升。
摘要：
Mamba架构因其线性计算复杂度而成为Transformer架构的有前景的替代方案，后者受困于二次复杂度。虽然现有工作主要集中于将Mamba适配为视觉编码器，但任务特定的Mamba解码器的关键作用仍未得到充分探索，尤其是对于易受畸变影响的密集预测任务。本文解决了两个相互关联的挑战：（1）设计一种基于Mamba的解码器，能够无缝适配各种架构（例如基于CNN、Transformer和Mamba的主干网络）；（2）在处理宽视场图像（例如180°鱼眼和360°全景设置）时，缺乏畸变感知能力的解码器性能下降的问题。我们提出了可变形Mamba解码器，这是一种高效的畸变感知解码器，将Mamba的计算效率与自适应畸变感知相结合。在五个宽视场分割基准上的综合实验验证了其有效性。值得注意的是，与广泛使用的解码器头相比，我们的解码器在360° Stanford2D3D分割基准上实现了+2.5%的性能提升，同时减少了72%的参数和97%的FLOPs。
英文摘要：
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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