Streaming Dense Video Captioning
<|reference_start|>Streaming Dense Video Captioning: 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.<|reference_end|>
@article{zhou2024streaming,
    title={Streaming Dense Video Captioning},
    author={Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan, Austin Myers,
  Xuehan Xiong, Arsha Nagrani, Cordelia Schmid},
    journal={arXiv preprint arXiv:2404.01297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01297},
    primaryClass={cs.CV}
}