Convolutional Module

Convolutional modules are fundamental building blocks in many deep learning architectures, primarily used for efficient feature extraction from data like images, audio, and point clouds. Current research focuses on enhancing convolutional modules by integrating them with other techniques, such as transformers and graph neural networks, to improve feature representation and address limitations like capturing long-range dependencies or handling noisy data. These advancements are driving improvements in various applications, including medical image analysis (e.g., lesion detection), speech recognition, and 3D scene reconstruction, by enabling more accurate and robust models.