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Distance transform regression for spatially-aware deep semantic segmentation

Abstract : Understanding visual scenes relies more and more on dense pixel-wise classification obtained via deep fully convolutional neural networks. However, due to the nature of the networks, predictions often suffer from blurry boundaries and ill-segmented shapes, fueling the need for post-processing. This work introduces a new semantic segmentation regularization based on the regression of a distance transform. After computing the distance transform on the label masks, we train a FCN in a multi-task setting in both discrete and continuous spaces by learning jointly classification and distance regression. This requires almost no modification of the network structure and adds a very low overhead to the training process. Learning to approximate the distance transform back-propagates spatial cues that implicitly regularizes the segmentation. We validate this technique with several architectures on various datasets, and we show significant improvements compared to competitive baselines.
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Contributor : Nicolas Audebert <>
Submitted on : Tuesday, September 3, 2019 - 5:19:19 PM
Last modification on : Friday, July 10, 2020 - 4:07:29 PM



Nicolas Audebert, Alexandre Boulch, Bertrand Le Saux, Sébastien Lefèvre. Distance transform regression for spatially-aware deep semantic segmentation. Computer Vision and Image Understanding, Elsevier, 2019, 189, pp.102809. ⟨10.1016/j.cviu.2019.102809⟩. ⟨hal-02277621⟩



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