An Efficient Channel Attention module, commonly known as an ECA module, is a lightweight architectural component in deep convolutional neural networks that improves model performance by learning the relative importance of different feature channels. It operates on spatially aggregated feature maps to capture local cross-channel interactions directly through a one-dimensional convolution without using dimensionality reduction. By dynamically determining the convolution kernel size as a function of the channel dimension, the module efficiently models dependencies among adjacent channels and generates weights that recalibrate the input feature maps, providing performance improvements while introducing negligible computational and parameter overhead.
