Ellipse detection presents a significant challenge in computer vision and pattern recognition, often hindered by traditional parameter regression methods that fail to account for the unique geometric characteristics and complex parameter interactions of ellipses. These limitations frequently result in imprecise detections, notably with small or partially occluded ellipses. To overcome these challenges, we propose EDNet, a novel ellipse detection network that exploits the geometric properties of ellipses, thus moving beyond the reliance on internal textures. EDNet improves ellipse detection by refining the loss function to better capture the relationship between the error and each parameters during training. It features a LoG-like Edge Detection Module (LEDM) and an Edge Guided Module (EGM) for precise boundary extraction and multi-scale feature enhancement. Additionally, an auxiliary component estimates ellipse vertices, boosting accuracy for occluded ellipses. Experimental results on two wildly-used benchmark datasets demonstrate that EDNet achieves significant improvements, with an average detection accuracy increase of 6% and 10% over leading state-of-the-art models. • We concentrate on the geometric characteristics of ellipse detection via Edge Detection Module and Edge Guided Module. • We design an auxiliary head for the estimation of four ellipse vertices, invoking additional feature attention on these pivotal points. • We establish the relations between the error and geometric characteristics of the ellipse by a model-aware loss function.

Keypoint estimation is a fundamental task in computer vision, but generally requires large-scale annotated data for training. Few-shot and unsupervised keypoint estimation are prevalent economical paradigms, but the former still requires annotations for extensive novel classes while the latter only supports for single class. In this paper, we focus on the task of weak-shot keypoint estimation, where multiple novel classes are learned from unlabeled images with the help of labeled base classes. The key problem is what to transfer from base classes to novel classes, and we propose to transfer keyness and correspondence, which essentially belong to comparing entities and thus are class-agnostic and class-wise transferable. The keyness compares which pixel in the local region is more key, which can guide the keypoints of novel classes to move towards the local maximum (i.e., obtaining keypoints). The correspondence compares whether the two pixels belongs to the same semantic part, which can activate the keypoints of novel classes by reinforcing the consistency between corresponding points on two paired images. By transferring keyness and correspondence, our framework achieves favourable performance for weak-shot keypoint estimation. Extensive experiments and analyses on large-scale benchmark MP-100 demonstrate our effectiveness.