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Open AccessFeature PaperArticle

Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
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Author to whom correspondence should be addressed.
Sensors 2019, 19(24), 5361; https://doi.org/10.3390/s19245361
Received: 25 October 2019 / Revised: 20 November 2019 / Accepted: 29 November 2019 / Published: 5 December 2019
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. View Full-Text
Keywords: semantic segmentation; computer vision; atrous convolution; spatial pooling semantic segmentation; computer vision; atrous convolution; spatial pooling
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MDPI and ACS Style

Artacho, B.; Savakis, A. Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation. Sensors 2019, 19, 5361. https://doi.org/10.3390/s19245361

AMA Style

Artacho B, Savakis A. Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation. Sensors. 2019; 19(24):5361. https://doi.org/10.3390/s19245361

Chicago/Turabian Style

Artacho, Bruno; Savakis, Andreas. 2019. "Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation" Sensors 19, no. 24: 5361. https://doi.org/10.3390/s19245361

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