  Deep convolutional models often produce inadequate predictions for inputs
foreign to the training distribution. Consequently, the problem of detecting
outlier images has recently been receiving a lot of attention. Unlike most
previous work, we address this problem in the dense prediction context in order
to be able to locate outlier objects in front of in-distribution background.
Our approach is based on two reasonable assumptions. First, we assume that the
inlier dataset is related to some narrow application field (e.g.~road driving).
Second, we assume that there exists a general-purpose dataset which is much
more diverse than the inlier dataset (e.g.~ImageNet-1k). We consider pixels
from the general-purpose dataset as noisy negative training samples since most
(but not all) of them are outliers. We encourage the model to recognize borders
between known and unknown by pasting jittered negative patches over inlier
training images. Our experiments target two dense open-set recognition
benchmarks (WildDash 1 and Fishyscapes) and one dense open-set recognition
dataset (StreetHazard). Extensive performance evaluation indicates competitive
potential of the proposed approach.
