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Dense pixelwise prediction such as semantic segmentation is an up-to-datechallenge for deep convolutional neural networks (CNNs). Many state-of-the-artapproaches either tackle the loss of high-resolution information due to poolingin the encoder stage, or use dilated convolutions or high-resolution lanes tomaintain detailed feature maps and predictions. Motivated by the structuralanalogy between multi-resolution wavelet analysis and the pooling/unpoolinglayers of CNNs, we introduce discrete wavelet transform (DWT) into the CNNencoder-decoder architecture and propose WCNN. The high-frequency waveletcoefficients are computed at encoder, which are later used at the decoder tounpooled jointly with coarse-resolution feature maps through the inverse DWT.The DWT/iDWT is further used to develop two wavelet pyramids to capture theglobal context, where the multi-resolution DWT is applied to successivelyreduce the spatial resolution and increase the receptive field. Experiment withthe Cityscape dataset, the proposed WCNNs are computationally efficient andyield improvements the accuracy for high-resolution dense pixelwise prediction.

Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform