A two-head segmentation model trained with pasted ImageNet negatives performs dense outlier detection alongside semantic segmentation in one forward pass and sets a new WildDash state of the art.
Robust Semantic Segmentation with Ladder-DenseNet Models
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abstract
We present semantic segmentation experiments with a model capable to perform predictions on four benchmark datasets: Cityscapes, ScanNet, WildDash and KITTI. We employ a ladder-style convolutional architecture featuring a modified DenseNet-169 model in the downsampling datapath, and only one convolution in each stage of the upsampling datapath. Due to limited computing resources, we perform the training only on Cityscapes Fine train+val, ScanNet train, WildDash val and KITTI train. We evaluate the trained model on the test subsets of the four benchmarks in concordance with the guidelines of the Robust Vision Challenge ROB 2018. The performed experiments reveal several interesting findings which we describe and discuss.
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Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
A two-head segmentation model trained with pasted ImageNet negatives performs dense outlier detection alongside semantic segmentation in one forward pass and sets a new WildDash state of the art.