  There has been a widely held view that visual representations (e.g.,
photographs and illustrations) do not depict negation, for example, one that
can be expressed by a sentence "the train is not coming". This view is
empirically challenged by analyzing the real-world visual representations of
comic (manga) illustrations. In the experiment using image captioning tasks, we
gave people comic illustrations and asked them to explain what they could read
from them. The collected data showed that some comic illustrations could depict
negation without any aid of sequences (multiple panels) or conventional devices
(special symbols). This type of comic illustrations was subjected to further
experiments, classifying images into those containing negation and those not
containing negation. While this image classification was easy for humans, it
was difficult for data-driven machines, i.e., deep learning models (CNN), to
achieve the same high performance. Given the findings, we argue that some comic
illustrations evoke background knowledge and thus can depict negation with
purely visual elements.
