Method Details

Details for method 'Axial-DeepLab-L [Cityscapes-fine]'


Method overview

name Axial-DeepLab-L [Cityscapes-fine]
challenge instance-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)
project page / code
used Cityscapes data fine annotations
used external data ImageNet
runtime n/a
subsampling no
submission date March, 2020
previous submissions


Average results

Metric Value
AP 33.2517
AP50% 54.9438
AP100m 48.7572
AP50m 51.9623


Class results

Class AP AP50% AP100m AP50m
person 31.9704 60.4837 51.2816 51.3372
rider 27.8489 60.5161 42.3996 42.9868
car 52.3964 74.0389 74.4562 77.148
truck 30.6836 39.4906 45.1815 54.3969
bus 44.1933 55.9174 66.5918 76.6773
train 33.966 53.4695 45.2847 47.3518
motorcycle 25.7373 50.1678 34.8293 35.6792
bicycle 19.2181 45.4667 30.0332 30.1213



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