Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Fishyscapes Benchmark: Measuring Blind Spots in Semantic
  Segmentation   Deep learning has enabled impressive progress in the accuracy of semantic
segmentation. Yet, the ability to estimate uncertainty and detect failure is
key for safety-critical applications like autonomous driving. Existing
uncertainty estimates have mostly been evaluated on simple tasks, and it is
unclear whether these methods generalize to more complex scenarios. We present
Fishyscapes, the first public benchmark for uncertainty estimation in a
real-world task of semantic segmentation for urban driving. It evaluates
pixel-wise uncertainty estimates towards the detection of anomalous objects in
front of the vehicle. We~adapt state-of-the-art methods to recent semantic
segmentation models and compare approaches based on softmax confidence,
Bayesian learning, and embedding density. Our results show that anomaly
detection is far from solved even for ordinary situations, while our benchmark
allows measuring advancements beyond the state-of-the-art.

16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th