Streaming Dense Video Captioning
Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan, Austin Myers, Xuehan Xiong, Arsha Nagrani, Cordelia Schmid
Abstract
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.
BibTeX
@inproceedings{cvpr2024_streamingdensevi,
title = {Streaming Dense Video Captioning},
author = {Xingyi Zhou and Anurag Arnab and Shyamal Buch and Shen Yan and Austin Myers and Xuehan Xiong and Arsha Nagrani and Cordelia Schmid},
booktitle = {CVPR 2024},
year = {2024}
}