SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection
This work addresses the need for efficient and generalizable edge detection in computer vision by reducing dependency on manual annotations, though it is incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of labor-intensive manual annotations in edge detection by proposing a self-supervised approach that transfers annotations from synthetic to real-world datasets, resulting in improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on BIPEDv2.
Edge detection is a fundamental technique in various computer vision tasks. Edges are indeed effectively delineated by pixel discontinuity and can offer reliable structural information even in textureless areas. State-of-the-art heavily relies on pixel-wise annotations, which are labor-intensive and subject to inconsistencies when acquired manually. In this work, we propose a novel self-supervised approach for edge detection that employs a multi-level, multi-homography technique to transfer annotations from synthetic to real-world datasets. To fully leverage the generated edge annotations, we developed SuperEdge, a streamlined yet efficient model capable of concurrently extracting edges at pixel-level and object-level granularity. Thanks to self-supervised training, our method eliminates the dependency on manual annotated edge labels, thereby enhancing its generalizability across diverse datasets. Comparative evaluations reveal that SuperEdge advances edge detection, demonstrating improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on BIPEDv2.