ADCNN: Towards learning adaptive dilation for convolutional neural networks. (March 2022) - Yao, Jie; Wang, Dongdong; Hu, Hao; Xing, Weiwei; Wang, Liqiang
Abstract: Dilated convolution kernels are constrained by their shared dilation, keeping them from being aware of diverse spatial contents at different locations. We address such limitations by formulating the dilation as trainable weights with respect to individual positions. We propose Adaptive Dilation Convolutional Neural Networks (ADCNN), a light-weighted extension that allows convolutional kernels to adjust their dilation value based on different contents at the pixel level. Unlike previous content-adaptive models, ADCNN dynamically infers pixel-wise dilation via modeling feed-forward inter-patterns, which provides a new perspective for developing adaptive network structures other than sampling kernel spaces. Our evaluation results indicate ADCNNs can be easily integrated into various backbone networks and consistently outperform their regular counterparts on various visual tasks.