DDConv: Dynamic Dilated Convolution

Dilated convolution is a powerful technique for expanding the receptive field without increasing the convolution kernel size, making it highly valuable in image segmentation tasks. However, some challenges still exist, such as sparse feature extraction at high dilation rates and limited neighboring information interaction. To address these issues, a novel approach, termed Dynamic Dilated Convolution (DDConv), is proposed for adaptive feature extraction by dynamically adjusting dilation rates in this paper. In DDConv, dilated convolutions are divided into four groups along the channel, each assigned distinct dilation rates. Attention weights are then used to aggregate information across these groups, enabling the convolution kernel to dynamically select the dilation rate during feature map convolution. Experimental results demonstrate the effectiveness of the proposed methods, with DDConv delivering outstanding performance. These findings suggest promising implications for DDNeXt as a novel backbone construction approach for future developments in the field.