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Semantic segmentation is a basic but non-trivial task in computer vision.Many previous work focus on utilizing affinity patterns to enhance segmentationnetworks. Most of these studies use the affinity matrix as a kind of featurefusion weights, which is part of modules embedded in the network, such asattention models and non-local models. In this paper, we associate affinitymatrix with labels, exploiting the affinity in a supervised way. Specifically,we utilize the label to generate a multi-scale label affinity matrix as astructural supervision, and we use a square root kernel to compute a non-localaffinity matrix on output layers. With such two affinities, we define a novelloss called Affinity Regression loss (AR loss), which can be an auxiliary lossproviding pair-wise similarity penalty. Our model is easy to train and addslittle computational burden without run-time inference. Extensive experimentson NYUv2 dataset and Cityscapes dataset demonstrate that our proposed method issufficient in promoting semantic segmentation networks.

Use square root affinity to regress labels in semantic segmentation