Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Benchmarking Robustness in Object Detection: Autonomous Driving when
  Winter is Coming   The ability to detect objects regardless of image distortions or weather
conditions is crucial for real-world applications of deep learning like
autonomous driving. We here provide an easy-to-use benchmark to assess how
object detection models perform when image quality degrades. The three
resulting benchmark datasets, termed Pascal-C, Coco-C and Cityscapes-C, contain
a large variety of image corruptions. We show that a range of standard object
detection models suffer a severe performance loss on corrupted images (down to
30--60\% of the original performance). However, a simple data augmentation
trick---stylizing the training images---leads to a substantial increase in
robustness across corruption type, severity and dataset. We envision our
comprehensive benchmark to track future progress towards building robust object
detection models. Benchmark, code and data are publicly available.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics