Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm

TL;DR

This paper introduces the Extractor-Selector paradigm, a pixel-wise feature selection framework that enhances edge detection accuracy by adaptively fusing features at each pixel, outperforming existing models across multiple benchmarks.

Contribution

The paper proposes a novel pixel-wise feature selection framework for edge detection, enabling more adaptive fusion and significant performance improvements without altering existing model architectures.

Findings

Achieves over 7% improvements in ODS and OIS on BIPED2 dataset.

Attains 22% improvement in Average Precision (AP).

Consistently outperforms baseline models across multiple benchmarks.

Abstract

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.