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We study the impact of visual assistance for automated audio captioning.Utilizing multi-encoder transformer architectures, which have previously beenemployed to introduce vision-related information in the context of sound eventdetection, we analyze the usefulness of incorporating a variety of pretrainedfeatures.  We perform experiments on a YouTube-based audiovisual data set andinvestigate the effect of applying the considered transfer learning techniquein terms of a variety of captioning metrics.  We find that only one of the considered kinds of pretrained features providesconsistent improvements, while the others do not provide any noteworthy gainsat all. Interestingly, the outcomes of prior research efforts indicate that theexact opposite is true in the case of sound event detection, leading us toconclude that the optimal choice of visual embeddings is strongly dependent onthe task at hand.  More specifically, visual features focusing on semantics appear appropriatein the context of automated audio captioning, while for sound event detection,time information seems to be more important.

Impact of visual assistance for automated audio captioning