Method Details
Details for method 'Hard Pixel Mining for Depth Privileged Semantic Segmentation'
Method overview
| name | Hard Pixel Mining for Depth Privileged Semantic Segmentation |
| challenge | pixel-level semantic labeling |
| details | Semantic segmentation has achieved remarkable progress but remains challenging due to the complex scene, object occlusion, and so on. Some research works have attempted to use extra information such as a depth map to help RGB based semantic segmentation because the depth map could provide complementary geometric cues. However, due to the inaccessibility of depth sensors, depth information is usually unavailable for the test images. In this paper, we leverage only the depth of training images as the privileged information to mine the hard pixels in semantic segmentation, in which depth information is only available for training images but not available for test images. Specifically, we propose a novel Loss Weight Module, which outputs a loss weight map by employing two depth-related measurements of hard pixels: Depth Prediction Error and Depthaware Segmentation Error. The loss weight map is then applied to segmentation loss, with the goal of learning a more robust model by paying more attention to the hard pixels. Besides, we also explore a curriculum learning strategy based on the loss weight map. Meanwhile, to fully mine the hard pixels on different scales, we apply our loss weight module to multi-scale side outputs. Our hard pixels mining method achieves the state-of-the-art results on three benchmark datasets, and even outperforms the methods which need depth input during testing. |
| publication | Hard Pixel Mining for Depth Privileged Semantic Segmentation Zhangxuan Gu, Li Niu, Haohua Zhao, and Liqing Zhang |
| project page / code | |
| used Cityscapes data | fine annotations, coarse annotations, stereo |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | July, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 83.3791 |
| iIoU Classes | 65.2467 |
| IoU Categories | 92.3387 |
| iIoU Categories | 82.6109 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.8128 | - |
| sidewalk | 87.8212 | - |
| building | 94.2875 | - |
| wall | 65.5797 | - |
| fence | 65.1612 | - |
| pole | 72.8538 | - |
| traffic light | 79.4759 | - |
| traffic sign | 82.3188 | - |
| vegetation | 94.2532 | - |
| terrain | 74.4412 | - |
| sky | 96.0928 | - |
| person | 88.6146 | 73.9742 |
| rider | 75.8121 | 57.7537 |
| car | 96.5669 | 91.8871 |
| truck | 77.8893 | 49.4111 |
| bus | 93.1813 | 63.8062 |
| train | 88.793 | 60.1939 |
| motorcycle | 73.0497 | 56.3989 |
| bicycle | 79.1971 | 68.5485 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.8274 | - |
| nature | 93.9424 | - |
| object | 78.193 | - |
| sky | 96.0928 | - |
| construction | 94.4359 | - |
| human | 88.6235 | 74.8795 |
| vehicle | 96.2561 | 90.3423 |