  To generate proper captions for videos, the inference needs to identify
relevant concepts and pay attention to the spatial relationships between them
as well as to the temporal development in the clip. Our end-to-end
encoder-decoder video captioning framework incorporates two transformer-based
architectures, an adapted transformer for a single joint spatio-temporal video
analysis as well as a self-attention-based decoder for advanced text
generation. Furthermore, we introduce an adaptive frame selection scheme to
reduce the number of required incoming frames while maintaining the relevant
content when training both transformers. Additionally, we estimate semantic
concepts relevant for video captioning by aggregating all ground truth captions
of each sample. Our approach achieves state-of-the-art results on the MSVD, as
well as on the large-scale MSR-VTT and the VATEX benchmark datasets considering
multiple Natural Language Generation (NLG) metrics. Additional evaluations on
diversity scores highlight the expressiveness and diversity in the structure of
our generated captions.
