提出像素级特征选择机制，让边缘检测更精准。
Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm
- 按像素动态选特征，而非统一融合，提升适应性。
- 在BIPED2上实现ODS/OIS提升超7%，AP提升22%。
- 可无缝接入现有模型，适合想提升边缘检测的开发者。
深度学习显著推动了图像边缘检测（ED）的发展，主要依赖于特征提取的改进。然而，现有大多数ED模型对所有像素采用统一的特征融合策略，忽略了边缘与纹理区域之间的关键差异。为此，我们提出提取器-选择器（Extractor-Selector, E-S）范式，引入像素级特征选择机制，实现更自适应、更精确的特征融合。不同于传统全局卷积核处理所有像素的方式，本方法在每个像素处动态选择相关特征，从而获得更精细的边缘预测。E-S框架可无修改地集成到现有ED模型中，带来显著性能提升，也可与增强型特征提取器结合以进一步提高精度。在多个基准数据集上的大量实验表明，该方法持续优于基线模型。例如，在BIPED2数据集上，该框架在ODS和OIS指标上提升超过7%，在AP指标上提升22%，充分验证了其有效性与优越性。
原文摘要 · Abstract (English)
Deep learning has significantly advanced image edge detection (ED), primarily through improved feature extraction. However, most existing ED models apply uniform feature fusion across all pixels, ignoring critical differences between regions such as edges and textures. To address this limitation, we propose the Extractor-Selector (E-S) paradigm, a novel framework that introduces pixel-wise feature selection for more adaptive and precise fusion. Unlike conventional image-level fusion that applies the same convolutional kernel to all pixels, our approach dynamically selects relevant features at each pixel, enabling more refined edge predictions. The E-S framework can be seamlessly integrated with existing ED models without architectural changes, delivering substantial performance gains. It can also be combined with enhanced feature extractors for further accuracy improvements. Extensive experiments across multiple benchmarks confirm that our method consistently outperforms baseline ED models. For instance, on the BIPED2 dataset, the proposed framework can achieve over 7$\%$ improvements in ODS and OIS, and 22$\%$ improvements in AP, demonstrating its effectiveness and superiority.
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