Atrous Convolution

Atrous convolution, a technique that modifies standard convolutions to increase receptive field size without increasing computational cost, is experiencing renewed interest in various computer vision tasks. Current research focuses on integrating atrous convolutions into advanced architectures like Vision Transformers and U-Nets, often within spatial pyramid pooling modules, to improve feature extraction and segmentation accuracy, particularly in challenging scenarios like medical image analysis and remote sensing. This enhanced ability to capture both local and global context through atrous convolutions leads to improved performance in tasks such as semantic segmentation, object detection, and instance segmentation, impacting fields ranging from autonomous driving to medical diagnostics.