任意坐标输入，精准语义分割，高效低内存。
CoordFormer: Give Me Any Coordinates and I Will Give You Labels
- 基于坐标编码的解码器，融合局部与全局特征。
- 在MaSS13K等数据集上达顶尖性能，计算量更低。
- 适合高分辨率图像分割，尤其边缘细节要求高的场景。
超高分辨率图像的语义分割因计算成本高、细粒度细节捕捉难而仍具挑战。本文提出CoordFormer，一种基于坐标的分割架构，通过配备局部交叉注意力机制的坐标解码器，在任意空间位置预测标签。该解码器结合坐标嵌入与高分辨率局部块特征，并与经ViT主干编码器下采样后提取的全局令牌交互，兼顾丰富语义上下文与像素级精度。此设计实现任意分辨率灵活推理，同时保持对超大图像输入的低内存占用；支持面向语义边界的高效策略，将计算集中于边界区域，既维持细粒度准确性，又降低延迟与计算开销。CoordFormer在MaSS13K上达到领先水平，且在DIS5K和KPIs上优于参数相当或更大的方法，证明其在高质量、超高清语义分割中的有效性。
原文摘要 · Abstract (English)
Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained details. We propose CoordFormer, a novel coordinate-based architecture for semantic segmentation that predicts labels at arbitrary spatial locations through a Coordinate Decoder equipped with a Localized Cross-Attention mechanism. The decoder combines coordinate embeddings with high-resolution local patch features and interacts with global tokens extracted from a downsampled image processed by a ViT foundation encoder, enabling rich semantic context while preserving pixel-level precision. This design enables flexible inference at arbitrary resolutions while keeping memory low on very-high-resolution inputs, and supports an efficient semantic-edge-focused strategy that concentrates computation along boundaries, maintaining fine-grained accuracy while reducing latency and computational cost. CoordFormer achieves state-of-the-art performance on MaSS13K and outperforms comparably sized and higher-parameter methods on DIS5K and KPIs, demonstrating its effectiveness for high-quality, very-high-resolution semantic segmentation.
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