CoordFormer: Give Me Any Coordinates and I Will Give You Labels

2026-09-08

AI Summary

针对高分辨率图像语义分割的计算成本高和细节捕捉难的问题，提出了一种基于坐标的新架构CoordFormer，通过局部交叉注意力机制实现任意位置的标签预测。

Abstract

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.

Problem

• semantic segmentation
• very-high-resolution images
• computational cost
• fine-grained details

Innovation

• Coordinate-based Architecture
• Localized Cross-Attention
• Semantic Segmentation
• High-resolution Images
• Flexible Inference