  Identifying unexpected objects on roads in semantic segmentation (e.g.,
identifying dogs on roads) is crucial in safety-critical applications. Existing
approaches use images of unexpected objects from external datasets or require
additional training (e.g., retraining segmentation networks or training an
extra network), which necessitate a non-trivial amount of labor intensity or
lengthy inference time. One possible alternative is to use prediction scores of
a pre-trained network such as the max logits (i.e., maximum values among
classes before the final softmax layer) for detecting such objects. However,
the distribution of max logits of each predicted class is significantly
different from each other, which degrades the performance of identifying
unexpected objects in urban-scene segmentation. To address this issue, we
propose a simple yet effective approach that standardizes the max logits in
order to align the different distributions and reflect the relative meanings of
max logits within each predicted class. Moreover, we consider the local regions
from two different perspectives based on the intuition that neighboring pixels
share similar semantic information. In contrast to previous approaches, our
method does not utilize any external datasets or require additional training,
which makes our method widely applicable to existing pre-trained segmentation
models. Such a straightforward approach achieves a new state-of-the-art
performance on the publicly available Fishyscapes Lost & Found leaderboard with
a large margin. Our code is publicly available at this
$\href{https://github.com/shjung13/Standardized-max-logits}{link}$.
