Adaptive Convolution

Adaptive convolution is a technique enhancing convolutional neural networks (CNNs) by dynamically adjusting convolution kernel parameters based on input features, improving adaptability to diverse data characteristics. Current research focuses on integrating adaptive convolutions into various architectures, including U-Nets and Transformers, to improve performance in tasks like image segmentation, time series forecasting, and object detection, often employing techniques like deformable convolutions and attention mechanisms. This approach leads to more robust and efficient models across diverse applications, particularly in medical image analysis and remote sensing, where data variability is significant.