Streaming Dense Video Captioning

• URL: http://arxiv.org/abs/2404.01297v1
• Date: Mon, 1 Apr 2024 17:59:15 GMT
• Title: Streaming Dense Video Captioning
• Authors: Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan, Austin Myers, Xuehan Xiong, Arsha Nagrani, Cordelia Schmid,
• Abstract summary: An ideal model for dense video captioning should be able to handle long input videos, predict rich, detailed textual descriptions. Current state-of-the-art models 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.
• Score: 85.70265343236687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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