  Explaining an image with missing or non-existent objects is known as object
bias (hallucination) in image captioning. This behaviour is quite common in the
state-of-the-art captioning models which is not desirable by humans. To
decrease the object hallucination in captioning, we propose three simple yet
efficient training augmentation method for sentences which requires no new
training data or increase in the model size. By extensive analysis, we show
that the proposed methods can significantly diminish our models' object bias on
hallucination metrics. Moreover, we experimentally demonstrate that our methods
decrease the dependency on the visual features. All of our code, configuration
files and model weights will be made public.
