Hybrid Encoder

Hybrid encoders combine the strengths of different neural network architectures, such as convolutional neural networks (CNNs) and transformers, to improve performance on various tasks. Current research focuses on leveraging CNNs' ability to capture local features and transformers' capacity for long-range dependencies, often within Siamese or U-shaped network structures, to achieve superior results in areas like image segmentation, change detection, and sequence tagging. This approach leads to more robust and efficient models across diverse applications, including medical image analysis and remote sensing, by effectively integrating complementary information processing capabilities.