Streaming Dense Video Captioning
Xingyi Zhou Anurag Arnab Shyamal Buch Shen Yan Austin Myers Xuehan Xiong Arsha Nagrani Cordelia Schmid
An ideal model for dense video captioning -- predicting captions localized
temporally in a video -- should be able to handle long input videos, predict
rich, detailed textual descriptions, and be able to produce outputs before
processing the entire video. Current state-of-the-art models, however, process
a fixed number of downsampled frames, and make a single full prediction after
seeing the whole video. We propose a streaming dense video captioning model
that consists of two novel components: First, we propose a new memory module,
based on clustering incoming tokens, which can handle arbitrarily long videos
as the memory is of a fixed size. Second, we develop a streaming decoding
algorithm that enables our model to make predictions before the entire video
has been processed. Our model achieves this streaming ability, and
significantly improves the state-of-the-art on three dense video captioning
benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at
https://github.com/google-research/scenic.