2 Dimensional Convolution

Two-dimensional convolution is a fundamental operation in convolutional neural networks (CNNs), primarily used for feature extraction from image-like data. Current research focuses on improving the efficiency and robustness of 2D convolutions, exploring alternative architectures like oriented 1D kernels and hybrid approaches combining 2D convolutions with other techniques (e.g., attention mechanisms, multi-task learning) to enhance performance in various applications. These advancements are driving improvements in diverse fields, including 3D object detection, medical image segmentation, and video recognition, by enabling faster, more accurate, and resource-efficient models.