Method Details
Details for method 'CABiNet'
Method overview
| name | CABiNet |
| challenge | pixel-level semantic labeling |
| details | With the increasing demand of autonomous machines, pixel-wise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for any potential real-time applications. In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network), a dual branch convolutional neural network (CNN), with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing multi-branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. Specifically, we achieve 76.6% and 75.9% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Codes and training models will be made publicly available. |
| publication | CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation Saumya Kumaar, Ye Lyu, Francesco Nex, Michael Ying Yang https://arxiv.org/abs/2011.00993 |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | |
| runtime | 0.013 s NVIDIA RTX2080Ti |
| subsampling | no |
| submission date | January, 2021 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 75.9588 |
| iIoU Classes | 48.9827 |
| IoU Categories | 91.0554 |
| iIoU Categories | 75.7435 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.1861 | - |
| sidewalk | 83.1818 | - |
| building | 92.9431 | - |
| wall | 44.1112 | - |
| fence | 62.0063 | - |
| pole | 71.0758 | - |
| traffic light | 78.3811 | - |
| traffic sign | 80.8305 | - |
| vegetation | 93.8981 | - |
| terrain | 70.9309 | - |
| sky | 95.6521 | - |
| person | 84.4958 | 61.334 |
| rider | 67.0964 | 41.6659 |
| car | 95.5884 | 90.9312 |
| truck | 59.9507 | 27.8904 |
| bus | 70.2586 | 38.3365 |
| train | 57.7441 | 32.7275 |
| motorcycle | 61.3951 | 34.7883 |
| bicycle | 75.4904 | 64.1876 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.6042 | - |
| nature | 93.3344 | - |
| object | 76.5972 | - |
| sky | 95.6521 | - |
| construction | 93.4715 | - |
| human | 84.9766 | 62.767 |
| vehicle | 94.7517 | 88.72 |