The paper "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks" introduces an Efficient Channel Attention (ECA) module designed to improve the performance of deep convolutional neural networks (CNNs) while maintaining low model complexity. The authors argue that existing channel attention mechanisms often increase model complexity, which is a trade-off that is not desirable. The ECA module avoids dimensionality reduction and captures cross-channel interaction through a fast 1D convolution, which is adaptively determined by the channel dimension. This approach ensures both efficiency and effectiveness. The ECA module is evaluated on various tasks, including image classification, object detection, and instance segmentation, using different CNN backbones such as ResNets and MobileNetV2. Experimental results show that the ECA module achieves significant performance improvements with significantly fewer parameters and computations compared to state-of-the-art methods. The paper also provides a detailed analysis of the ECA module's effectiveness and discusses its implementation details.The paper "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks" introduces an Efficient Channel Attention (ECA) module designed to improve the performance of deep convolutional neural networks (CNNs) while maintaining low model complexity. The authors argue that existing channel attention mechanisms often increase model complexity, which is a trade-off that is not desirable. The ECA module avoids dimensionality reduction and captures cross-channel interaction through a fast 1D convolution, which is adaptively determined by the channel dimension. This approach ensures both efficiency and effectiveness. The ECA module is evaluated on various tasks, including image classification, object detection, and instance segmentation, using different CNN backbones such as ResNets and MobileNetV2. Experimental results show that the ECA module achieves significant performance improvements with significantly fewer parameters and computations compared to state-of-the-art methods. The paper also provides a detailed analysis of the ECA module's effectiveness and discusses its implementation details.