## Idea

They propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. They define loss functions at several stages in the network to provide supervisions at different stages.

## Network

The convolution and subsampling operations motivate the LRN architecture, the feature map $$f(I)$$ obtained at the end of the encoder network mainly contains high-level information about the image. Spatially precise information is lost in the encoder network, and therefore $$f(I)$$ cannot be used directly to recover a full-sized semantic segmentation which requires pixel-precise information. $$f(I)$$ can be used to produce a segmentation map $$s(I)$$ of spatial dimensions $$h_0 \times w_0$$ , which is smaller than the original image dimensions $$h \times w$$. The costom decoder network progressively refines the segmentation map $$s(I)$$. This model enforces the channel dimension of $$s_k (I)$$ to be the same as the number of class labels, so $$s_k (I)$$ can be considered as a (soft) label map.

## Results

The model produces segmentation labels in a coarse-to-fine manner. The segmentation labels at coarse levels are used to refine the labeling produced at finer levels progressively.