This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented as one stream from the input to the output with subsampling operators applied in the stream in order to reduce the feature maps size and to increase the receptive field for the final prediction. However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction. To tackle this problem, our GridNet follows a grid pattern allowing multiple interconnected streams to work at different resolutions. We show that our network generalizes many well known networks such as conv-deconv, residual or U-Net networks. GridNet is trained from scratch and achieves competitive results on the Cityscapes dataset.
@cite_24: This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a "stacked hourglass" network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.
All of these networks are based on the idea that subsampling is important to increase the receptive field and try to override the side effect of resolution loss with deconvolutionnal technics. In our , composed of multiple streams working at different feature map sizes, we use the subsampling and upsampling operators as connectors between streams allowing the network to take decisions at any resolution. The upsampling operators are not used to correct this side effect but to allow multi-scale decisions in the network. In a recent work, Newell al @cite_24 stacked many U-Net showing that successive steps of subsampling and upsampling are important to improve the performance of the network. This idea is improved in our with the strong connections between streams.