We propose a new efficient architecture for semantic segmentation, based on a "Waterfall" Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.

我们提出了一种新的有效的语义分割架构-瀑布空洞空间池化架构，它在减少网络参数和内存占用的同时实现了显着的准确性提高。该架构利用级联架构中渐进过滤的效率，同时保持与空间金字塔配置相当的多尺度视场。 此外，我们的方法不依赖于具有条件随机场的后处理阶段，进一步减少了复杂性和所需的训练时间。我们证明，具有ResNet骨干结构的瀑布方法是一种稳健高效的语义分割架构，在Pascal VOC和Cityscapes数据集上获得了最先进的结果，并显著减少了参数数量。

用于高效语义分割的瀑布空洞空间池化架构