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Paper · 2302.14115 · CVPR · 2023

Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning

Cordelia Schmid, Arsha Nagrani, Ivan Laptev, Antoine Miech, Antoine Yang, Josef Sivic, Paul, Jordi Pont-Tuset, Hongsuck Seo

arXiv · PDF · Open in the Atlas

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Abstract

Hey guys today I am going to teach you how to ski The kids make it look easy First slope, congratz! <1s><8s>The man is fastening the dog. <20s><50s>The dogs are pulling the sled. <45s><49s>The man is saying hello. Figure 1. Vid2Seq is a visual language model that predicts dense event captions together with their temporal grounding in the video by generating a single sequence of tokens (right). This ability is enabled by large-scale pretraining on unlabeled narrated videos (left).

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