Method Details

Details for method 'AdapNet++'

name	AdapNet++
challenge	pixel-level semantic labeling
details	In this work, we propose the AdapNet++ architecture for semantic segmentation that aims to achieve the right trade-off between performance and computational complexity of the model. AdapNet++ incorporates a new encoder with multiscale residual units and an efficient atrous spatial pyramid pooling (eASPP) module that has a larger effective receptive field with more than 10x fewer parameters compared to the standard ASPP, complemented with a strong decoder with a multi-resolution supervision scheme that recovers high-resolution details. Comprehensive empirical evaluations on the challenging Cityscapes, Synthia, SUN RGB-D, ScanNet and Freiburg Forest datasets demonstrate that our architecture achieves state-of-the-art performance while simultaneously being efficient in terms of both the number of parameters and inference time. Please refer to https://arxiv.org/abs/1808.03833 for details. A live demo on various datasets can be viewed at http://deepscene.cs.uni-freiburg.de
publication	Self-Supervised Model Adaptation for Multimodal Semantic Segmentation Abhinav Valada, Rohit Mohan, Wolfram Burgard IJCV 2019 https://arxiv.org/abs/1808.03833
project page / code	http://deepscene.cs.uni-freiburg.de
used Cityscapes data	fine annotations, coarse annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	January, 2019
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