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The state-of-the-art object detection and image classification methods canperform impressively on more than 9k and 10k classes, respectively. Incontrast, the number of classes in semantic segmentation datasets is relativelylimited. This is not surprising when the restrictions caused by the lack oflabeled data and high computation demand for segmentation are considered. Inthis paper, we propose a novel training methodology to train and scale theexisting semantic segmentation models for a large number of semantic classeswithout increasing the memory overhead. In our embedding-based scalablesegmentation approach, we reduce the space complexity of the segmentationmodel's output from O(C) to O(1), propose an approximation method forground-truth class probability, and use it to compute cross-entropy loss. Theproposed approach is general and can be adopted by any state-of-the-artsegmentation model to gracefully scale it for any number of semantic classeswith only one GPU. Our approach achieves similar, and in some cases, evenbetter mIoU for Cityscapes, Pascal VOC, ADE20k, COCO-Stuff10k datasets whenadopted to DeeplabV3+ model with different backbones. We demonstrate a clearbenefit of our approach on a dataset with 1284 classes, bootstrapped from LVISand COCO annotations, with three times better mIoU than the DeeplabV3+ model.

Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU