  Dense video captioning is the task that involves the detection and
description of events within video sequences. While traditional approaches
focus on offline solutions where the entire video of analysis is available for
the captioning model, in this work we introduce a paradigm shift towards Live
Video Captioning (LVC). In LVC, dense video captioning models must generate
captions for video streams in an online manner, facing important constraints
such as having to work with partial observations of the video, the need for
temporal anticipation and, of course, ensuring ideally a real-time response. In
this work we formally introduce the novel problem of LVC and propose new
evaluation metrics tailored for the online scenario, demonstrating their
superiority over traditional metrics. We also propose an LVC model integrating
deformable transformers and temporal filtering to address the LVC new
challenges. Experimental evaluations on the ActivityNet Captions dataset
validate the effectiveness of our approach, highlighting its performance in LVC
compared to state-of-the-art offline methods. Results of our model as well as
an evaluation kit with the novel metrics integrated are made publicly available
to encourage further research on LVC.
