Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Semantic Segmentation with Ladder-DenseNet Models   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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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: dg-ga