  With the advent of rich visual representations and pre-trained language
models, video captioning has seen continuous improvement over time. Despite the
performance improvement, video captioning models are prone to hallucination.
Hallucination refers to the generation of highly pathological descriptions that
are detached from the source material. In video captioning, there are two kinds
of hallucination: object and action hallucination. Instead of endeavoring to
learn better representations of a video, in this work, we investigate the
fundamental sources of the hallucination problem. We identify three main
factors: (i) inadequate visual features extracted from pre-trained models, (ii)
improper influences of source and target contexts during multi-modal fusion,
and (iii) exposure bias in the training strategy. To alleviate these problems,
we propose two robust solutions: (a) the introduction of auxiliary heads
trained in multi-label settings on top of the extracted visual features and (b)
the addition of context gates, which dynamically select the features during
fusion. The standard evaluation metrics for video captioning measures
similarity with ground truth captions and do not adequately capture object and
action relevance. To this end, we propose a new metric, COAHA (caption object
and action hallucination assessment), which assesses the degree of
hallucination. Our method achieves state-of-the-art performance on the
MSR-Video to Text (MSR-VTT) and the Microsoft Research Video Description Corpus
(MSVD) datasets, especially by a massive margin in CIDEr score.
