  Current movie captioning architectures are not capable of mentioning
characters with their proper name, replacing them with a generic "someone" tag.
The lack of movie description datasets with characters' visual annotations
surely plays a relevant role in this shortage. Recently, we proposed to extend
the M-VAD dataset by introducing such information. In this paper, we present an
improved version of the dataset, namely M-VAD Names, and its semi-automatic
annotation procedure. The resulting dataset contains 63k visual tracks and 34k
textual mentions, all associated with character identities. To showcase the
features of the dataset and quantify the complexity of the naming task, we
investigate multimodal architectures to replace the "someone" tags with proper
character names in existing video captions. The evaluation is further extended
by testing this application on videos outside of the M-VAD Names dataset.
