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
this https URL

提出了一种用于密集视频字幕生成的理想模型，能够处理长时间视频输入、预测丰富详细的文本描述，并且能在整个视频处理完成之前生成输出，通过引入聚类处理令其能够处理任意长的视频，并设计了一种流式解码算法使模型能够提前进行预测，实验证明了该模型在三个密集视频字幕生成基准数据集上优于现有的最先进模型。