ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Understand

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs).

• However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity.
• To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain.
• By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity.