Researchers have developed a new type of convolutional kernel called Core-KAN that can adapt to varying scales and structures in images. Unlike traditional kernels, which are fixed on discrete grids, Core-KAN uses a continuous representation to synthesize spatial filters at arbitrary resolutions. This allows it to decouple geometric scale adaptation from content-dependent filtering, improving efficiency and reducing computational cost. Experiments show that Core-KAN outperforms strong baselines with only marginal overhead. --- Why it matters: This matters because traditional convolutional kernels can struggle with complex image structures, leading to suboptimal performance. Core-KAN's ability to adapt to varying scales and structures could improve the accuracy of computer vision tasks such as object detection and segmentation. Source: https://arxiv.org/abs/2608.19817