  Video-to-Text (VTT) is the task of automatically generating descriptions for
short audio-visual video clips, which can support visually impaired people to
understand scenes of a YouTube video for instance. Transformer architectures
have shown great performance in both machine translation and image captioning,
lacking a straightforward and reproducible application for VTT. However, there
is no comprehensive study on different strategies and advice for video
description generation including exploiting the accompanying audio with fully
self-attentive networks. Thus, we explore promising approaches from image
captioning and video processing and apply them to VTT by developing a
straightforward Transformer architecture. Additionally, we present a novel way
of synchronizing audio and video features in Transformers which we call
Fractional Positional Encoding (FPE). We run multiple experiments on the VATEX
dataset to determine a configuration applicable to unseen datasets that helps
describe short video clips in natural language and improved the CIDEr and
BLEU-4 scores by 37.13 and 12.83 points compared to a vanilla Transformer
network and achieve state-of-the-art results on the MSR-VTT and MSVD datasets.
Also, FPE helps increase the CIDEr score by a relative factor of 8.6%.
