  Dense video captioning aims to localize and describe important events in
untrimmed videos. Existing methods mainly tackle this task by exploiting only
visual features, while completely neglecting the audio track. Only a few prior
works have utilized both modalities, yet they show poor results or demonstrate
the importance on a dataset with a specific domain. In this paper, we introduce
Bi-modal Transformer which generalizes the Transformer architecture for a
bi-modal input. We show the effectiveness of the proposed model with audio and
visual modalities on the dense video captioning task, yet the module is capable
of digesting any two modalities in a sequence-to-sequence task. We also show
that the pre-trained bi-modal encoder as a part of the bi-modal transformer can
be used as a feature extractor for a simple proposal generation module. The
performance is demonstrated on a challenging ActivityNet Captions dataset where
our model achieves outstanding performance. The code is available:
v-iashin.github.io/bmt
