Method Details

Details for method 'Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas + Pseudo-labels]'

name	Panoptic-DeepLab w/ SWideRNet [Mapillary Vistas + Pseudo-labels]
challenge	pixel-level semantic labeling
details	We revisit the architecture design of Wide Residual Networks. We design a baseline model by incorporating the simple and effective Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets. Its network capacity is further scaled up or down by adjusting the width (i.e., channel size) and depth (i.e., number of layers), resulting in a family of SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such a simple scaling scheme, coupled with grid search, identifies several SWideRNets that significantly advance state-of-the-art performance on panoptic segmentation datasets in both the fast model regime and strong model regime. Following Naive-Student, this model is additionally trained with pseudo-labels generated from Cityscapes Video and train-extra set (i.e., the coarse annotations are not used, but the images are).
publication	Scaling Wide Residual Networks for Panoptic Segmentation Liang-Chieh Chen, Huiyu Wang, Siyuan Qiao https://arxiv.org/abs/2011.11675
project page / code
used Cityscapes data	fine annotations, video
used external data	ImageNet, Mapillary Vistas Research Edition. Cityscapes train-extra set (coarse labels are not used but only images).
runtime	n/a
subsampling	no
submission date	January, 2021
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