  We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics for comparing automatic video captions against a manual caption such as
BLEU and METEOR, drawn from techniques used in evaluating machine translation,
were used in the TRECVid video captioning task in 2016 but these are shown to
have weaknesses. The work presented here brings human assessment into the
evaluation by crowdsourcing how well a caption describes a video. We
automatically degrade the quality of some sample captions which are assessed
manually and from this we are able to rate the quality of the human assessors,
a factor we take into account in the evaluation. Using data from the TRECVid
video-to-text task in 2016, we show how our direct assessment method is
replicable and robust and should scale to where there many caption-generation
techniques to be evaluated.
