Efficient semantic image segmentation with superpixel pooling
This work addresses efficiency and accuracy improvements in semantic segmentation for computer vision applications, but it is incremental as it builds on existing network architectures.
The paper tackles semantic image segmentation by evaluating superpixel pooling layers in deep networks, showing that this approach consistently increases accuracy with minimal computational overhead on datasets like IBSR and Cityscapes.
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