  Image captioning research achieved breakthroughs in recent years by
developing neural models that can generate diverse and high-quality
descriptions for images drawn from the same distribution as training images.
However, when facing out-of-distribution (OOD) images, such as corrupted
images, or images containing unknown objects, the models fail in generating
relevant captions.
  In this paper, we consider the problem of OOD detection in image captioning.
We formulate the problem and suggest an evaluation setup for assessing the
model's performance on the task. Then, we analyze and show the effectiveness of
the caption's likelihood score at detecting and rejecting OOD images, which
implies that the relatedness between the input image and the generated caption
is encapsulated within the score.
