Method Details

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


Method overview

name Axial-DeepLab-XL [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 April, 2020
previous submissions


Average results

Metric Value
AP 39.6336
AP50% 64.168
AP100m 55.479
AP50m 57.4197


Class results

Class AP AP50% AP100m AP50m
person 36.5754 66.1173 56.2342 56.3886
rider 32.4889 65.5408 47.8991 48.3985
car 56.5951 78.9779 77.8155 80.1814
truck 40.9977 52.9021 55.8824 62.1738
bus 52.4269 67.7541 73.5189 78.2301
train 43.6786 65.9702 57.2532 58.0859
motorcycle 30.8181 62.4789 39.6706 40.4505
bicycle 23.4883 53.6024 35.558 35.4492



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