  Automatic Target Recognition (ATR) is a category of computer vision
algorithms which attempts to recognize targets on data obtained from different
sensors. ATR algorithms are extensively used in real-world scenarios such as
military and surveillance applications. Existing ATR algorithms are developed
for traditional closed-set methods where training and testing have the same
class distribution. Thus, these algorithms have not been robust to unknown
classes not seen during the training phase, limiting their utility in
real-world applications. To this end, we propose an Open-set Automatic Target
Recognition framework where we enable open-set recognition capability for ATR
algorithms. In addition, we introduce a plugin Category-aware Binary Classifier
(CBC) module to effectively tackle unknown classes seen during inference. The
proposed CBC module can be easily integrated with any existing ATR algorithms
and can be trained in an end-to-end manner. Experimental results show that the
proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10
datasets. To the best of our knowledge, this is the first work to address the
open-set classification problem for ATR algorithms. Source code is available
at: https://github.com/bardisafa/Open-set-ATR.
