Method Details
Details for method 'Axial-DeepLab-L [Cityscapes-fine]'
Method overview
| name | Axial-DeepLab-L [Cityscapes-fine] |
| challenge | pixel-level semantic labeling |
| details | Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions. This reduces computation complexity and allows performing attention within a larger or even global region. In companion, we also propose a position-sensitive self-attention design. Combining both yields our position-sensitive axial-attention layer, a novel building block that one could stack to form axial-attention models for image classification and dense prediction. We demonstrate the effectiveness of our model on four large-scale datasets. In particular, our model outperforms all existing stand-alone self-attention models on ImageNet. Our Axial-DeepLab improves 2.8% PQ over bottom-up state-of-the-art on COCO test-dev. This previous state-of-the-art is attained by our small variant that is 3.8x parameter-efficient and 27x computation-efficient. Axial-DeepLab also achieves state-of-the-art results on Mapillary Vistas and Cityscapes. |
| publication | Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen ECCV 2020 (spotlight) https://arxiv.org/abs/2003.07853 |
| project page / code | https://github.com/csrhddlam/axial-deeplab |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | March, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 79.4968 |
| iIoU Classes | 57.4839 |
| IoU Categories | 91.4892 |
| iIoU Categories | 76.2137 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.6313 | - |
| sidewalk | 86.5022 | - |
| building | 93.4365 | - |
| wall | 52.0461 | - |
| fence | 61.3156 | - |
| pole | 70.1632 | - |
| traffic light | 77.5351 | - |
| traffic sign | 81.0828 | - |
| vegetation | 93.4519 | - |
| terrain | 72.3189 | - |
| sky | 95.6783 | - |
| person | 87.9289 | 68.2537 |
| rider | 75.6083 | 55.4641 |
| car | 96.0295 | 84.76 |
| truck | 68.4795 | 39.7625 |
| bus | 81.3838 | 55.8922 |
| train | 77.0785 | 51.1847 |
| motorcycle | 71.0344 | 51.0277 |
| bicycle | 70.7347 | 53.5266 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.6638 | - |
| nature | 93.1728 | - |
| object | 76.0034 | - |
| sky | 95.6783 | - |
| construction | 93.6072 | - |
| human | 88.0594 | 69.6875 |
| vehicle | 95.2395 | 82.7399 |