Method Details

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


Method overview

name Axial-DeepLab-XL [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 33.9951
AP50% 55.8674
AP100m 49.6014
AP50m 53.1034


Class results

Class AP AP50% AP100m AP50m
person 32.3156 61.2127 51.4414 51.5652
rider 28.1706 59.2245 42.4664 42.8731
car 52.6413 74.9595 74.4193 77.103
truck 32.64 41.7007 47.502 57.4945
bus 41.7751 52.9174 63.5619 73.4058
train 38.0636 57.9667 50.1989 54.0729
motorcycle 25.8692 51.9398 35.5985 36.5713
bicycle 20.4851 47.0178 31.6226 31.7412



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