Spatial Convolution

Spatial convolution, a fundamental operation in many machine learning models, aims to efficiently extract spatial features from data like images and point clouds. Current research focuses on improving efficiency and adaptability of spatial convolutions, exploring techniques like spatially-varying kernels, filter subspaces for efficient fine-tuning, and integrating them with transformer architectures for spatio-temporal modeling in video and point cloud sequences. These advancements are driving improvements in various applications, including image restoration, 3D object detection, and human pose estimation, by enabling more accurate and computationally efficient models.