Method Details

Details for method 'ScaleNet'

name	ScaleNet
challenge	pixel-level semantic labeling
details	The scale difference in driving scenarios is one of the essential challenges in semantic scene segmentation. Close objects cover significantly more pixels than far objects. In this paper, we address this challenge with a scale invariant architecture. Within this architecture, we explicitly estimate the depth and adapt the pooling field size accordingly. Our model is compact and can be extended easily to other research domains. Finally, the accuracy of our approach is comparable to the state-of-the-art and superior for scale problems. We evaluate on the widely used automotive dataset Cityscapes as well as a self-recorded dataset.
publication	ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes Mohammad Dawud Ansari, Stephan Krarß, Oliver Wasenmüller and Didier Stricker International Conference on Computer Vision Theory and Applications, Funchal, Portugal, 2018
project page / code
used Cityscapes data	fine annotations, coarse annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	October, 2017
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