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arXiv · 2411.16481

Deformable Mamba for Wide Field of View Segmentation

Jie Hu·Junwei Zheng·Jiale Wei·Jiaming Zhang·Rainer Stiefelhagen
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Abstract

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{\deg} fisheye and 360{\deg} 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{\deg} Stanford2D3D segmentation benchmark while reducing 72% parameters and 97% FLOPs, as compared to the widely-used decoder heads.

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Jie Hu, Junwei Zheng, Jiale Wei, Jiaming Zhang, Rainer Stiefelhagen. 2024-11-25. Deformable Mamba for Wide Field of View Segmentation. https://arxiv.org/abs/2411.16481

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