Method Details

Details for method 'Axial-DeepLab-L [Mapillary Vistas]'


Method overview

name Axial-DeepLab-L [Mapillary Vistas]
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, Mapillary Vistas
runtime n/a
subsampling no
submission date March, 2020
previous submissions


Average results

Metric Value
AP 38.1241
AP50% 61.5593
AP100m 54.3366
AP50m 57.3045


Class results

Class AP AP50% AP100m AP50m
person 34.692 64.0315 54.1884 54.3205
rider 30.4258 62.7993 45.1311 45.6348
car 55.1278 77.3013 76.6106 78.9244
truck 40.8661 52.7471 57.4497 63.4692
bus 49.6592 63.0882 73.0831 83.637
train 43.5148 63.1273 58.1232 61.6633
motorcycle 28.9583 58.7787 36.9672 37.5543
bicycle 21.7486 50.6012 33.1392 33.2322



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