Method Details

Details for method 'DeepLabv3'

name	DeepLabv3
challenge	pixel-level semantic labeling
details	In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter’s field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we employ a module, called Atrous Spatial Pyrmid Pooling (ASPP), which adopts atrous convolution in parallel to capture multi-scale context with multiple atrous rates. Furthermore, we propose to augment ASPP module with image-level features encoding global context and further boost performance. Results obtained with a single model (no ensemble), trained with fine + coarse annotations. More details will be shown in the updated arXiv report.
publication	Rethinking Atrous Convolution for Semantic Image Segmentation Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam arXiv preprint https://arxiv.org/abs/1706.05587
project page / code
used Cityscapes data	fine annotations, coarse annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	September, 2017
previous submissions