Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm
This addresses a limitation in edge detection models for computer vision applications, offering an incremental improvement through a novel fusion method.
The paper tackled the problem of uniform feature fusion in deep learning-based image edge detection by proposing the Extractor-Selector paradigm, which introduces pixel-wise feature selection for adaptive fusion, resulting in over 7% improvements in ODS and OIS and 22% in AP on the BIPED2 dataset.
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.