Method Details
Details for method 'Axial-DeepLab-L [Mapillary Vistas]'
Method overview
| name | Axial-DeepLab-L [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 | March, 2020 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 83.1423 |
| iIoU Classes | 64.023 |
| IoU Categories | 92.3349 |
| iIoU Categories | 78.9062 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 98.8119 | - |
| sidewalk | 87.8007 | - |
| building | 94.1522 | - |
| wall | 59.7815 | - |
| fence | 68.0794 | - |
| pole | 73.4194 | - |
| traffic light | 79.4888 | - |
| traffic sign | 82.6183 | - |
| vegetation | 93.948 | - |
| terrain | 72.7257 | - |
| sky | 96.0848 | - |
| person | 88.9012 | 71.1657 |
| rider | 77.5147 | 58.2308 |
| car | 96.4877 | 87.0429 |
| truck | 76.8575 | 51.2779 |
| bus | 91.2153 | 64.0995 |
| train | 92.6248 | 63.8267 |
| motorcycle | 74.7286 | 57.8978 |
| bicycle | 74.4632 | 58.6424 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.7371 | - |
| nature | 93.6701 | - |
| object | 78.642 | - |
| sky | 96.0848 | - |
| construction | 94.4179 | - |
| human | 88.8617 | 72.3049 |
| vehicle | 95.931 | 85.5074 |