Papers › Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
Bruno Artacho, Andreas Savakis
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | Cityscapes test | WASPnet (ours) | Mean IoU (class) | 70.5% | #81 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | WASPnet (ours) | mIoU | 74% | #78 of 99 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | WASPnet-CRF (ours) | Mean IoU | 79.6% | #32 of 51 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | WASPnet-CRF (ours) | mIoU | 80.41% | #11 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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