  Characters are an important aspect of any storyline and identifying and
including them in descriptions is necessary for story understanding. While
previous work has largely ignored identity and generated captions with someone
(anonymized names), recent work formulates id-aware captioning as a
fill-in-the-blanks (FITB) task, where, given a caption with blanks, the goal is
to predict person id labels. However, to predict captions with ids, a two-stage
approach is required: first predict captions with someone, then fill in
identities. In this work, we present a new single stage approach that can
seamlessly switch between id-aware caption generation or FITB when given a
caption with blanks. Our model, Movie-Identity Captioner (MICap), uses a shared
auto-regressive decoder that benefits from training with FITB and full-caption
generation objectives, while the encoder can benefit from or disregard captions
with blanks as input. Another challenge with id-aware captioning is the lack of
a metric to capture subtle differences between person ids. To this end, we
introduce iSPICE, a caption evaluation metric that focuses on identity tuples
created through intermediate scene graphs. We evaluate MICap on Large-Scale
Movie Description Challenge (LSMDC), where we show a 4.2% improvement in FITB
accuracy, and a 1-2% bump in classic captioning metrics.
