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[bibtex]@InProceedings{Dhingra_2021_ICCV, author = {Dhingra, Naina and Chogovadze, George and Kunz, Andreas}, title = {Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline Using Graph Convolutional Network}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {865-875} }
Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline Using Graph Convolutional Network
Abstract
We present Border-SegGCN, a novel architecture to improve semantic segmentation by refining the border outline using graph convolutional networks (GCN). The semantic segmentation network such as UNet or DeepLabV3+ is used as a base network to have pre-segmented output. This output is converted into a graphical structure and fed into the GCN to improve the border pixel prediction of the pre-segmented output. We explored and studied the factors such as border thickness, number of edges for a node, and the number of features to be fed into the GCN by performing experiments. We demonstrate the effectiveness of the Border-SegGCN on the CamVid and Carla dataset, achieving a test set performance of 81.96% without any post-processing on CamVid dataset. It is higher than the reported state of the art mIoU achieved on CamVid dataset by 0.404%.
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