Method Details
Details for method 'Axial-DeepLab-XL [Mapillary Vistas]'
Method overview
| name | Axial-DeepLab-XL [Mapillary Vistas] |
| 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, Mapillary Vistas |
| runtime | n/a |
| subsampling | no |
| submission date | April, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 84.081 |
| iIoU Classes | 65.9748 |
| IoU Categories | 92.6413 |
| iIoU Categories | 79.7343 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.8595 | - |
| sidewalk | 88.3354 | - |
| building | 94.5099 | - |
| wall | 69.0562 | - |
| fence | 67.8041 | - |
| pole | 74.4834 | - |
| traffic light | 80.2845 | - |
| traffic sign | 83.8604 | - |
| vegetation | 94.1366 | - |
| terrain | 72.8179 | - |
| sky | 96.1401 | - |
| person | 89.2767 | 72.0482 |
| rider | 78.2424 | 59.7257 |
| car | 96.4119 | 87.5444 |
| truck | 76.3721 | 51.742 |
| bus | 93.0339 | 69.2707 |
| train | 91.3078 | 66.711 |
| motorcycle | 75.674 | 59.596 |
| bicycle | 76.9331 | 61.1603 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7754 | - |
| nature | 93.8613 | - |
| object | 79.5828 | - |
| sky | 96.1401 | - |
| construction | 94.6956 | - |
| human | 89.298 | 73.1755 |
| vehicle | 96.1355 | 86.2931 |