In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool

翻译：在这项工作中,我们评估了在深网络结构中使用超级像素集合层进行语义分离的情况。超级像素集合是一个灵活而高效的替代方法,可以替代包含先前空间信息的其他集合战略。我们建议对层进行简单而高效的GPU实施,并探索将层纳入现有网络结构的若干设计。我们提供了关于IBSR和城市景观数据集的实验结果,表明可以利用超级像素集合来不断提高网络准确性,同时尽量减少计算间接费用。源代码见https://github.com/bermanmaxim/superpixpool。