Method Details
Details for method 'SPGNet'
Method overview
| name | SPGNet |
| challenge | pixel-level semantic labeling |
| details | Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi-stage encoder-decoder networks have been widely used in human pose estimation and show superior performance than their single-stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. We find that by carefully re-weighting features across stages, a two-stage encoder-decoder network coupled with our proposed SPG module can significantly outperform its one-stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only 'fine' annotations. |
| publication | SPGNet: Semantic Prediction Guidance for Scene Parsing Bowen Cheng, Liang-Chieh Chen, Yunchao Wei, Yukun Zhu, Zilong Huang, Jinjun Xiong, Thomas Huang, Wen-Mei Hwu, Honghui Shi ICCV 2019 https://arxiv.org/abs/1908.09798 |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | March, 2019 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 81.0901 |
| iIoU Classes | 61.447 |
| IoU Categories | 92.1467 |
| iIoU Categories | 82.0908 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.7924 | - |
| sidewalk | 87.605 | - |
| building | 93.7729 | - |
| wall | 56.4799 | - |
| fence | 61.9184 | - |
| pole | 71.8958 | - |
| traffic light | 79.9507 | - |
| traffic sign | 82.0779 | - |
| vegetation | 94.0825 | - |
| terrain | 73.5125 | - |
| sky | 96.099 | - |
| person | 88.6817 | 73.4989 |
| rider | 74.9341 | 54.8083 |
| car | 96.4726 | 91.5607 |
| truck | 67.3384 | 42.5601 |
| bus | 84.8188 | 57.6436 |
| train | 81.7982 | 53.6576 |
| motorcycle | 71.1094 | 51.2846 |
| bicycle | 79.3718 | 66.5625 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7939 | - |
| nature | 93.7539 | - |
| object | 77.4615 | - |
| sky | 96.099 | - |
| construction | 94.2022 | - |
| human | 88.8411 | 74.4925 |
| vehicle | 95.8751 | 89.6891 |