VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer
Abstract
We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available at https://github.com/pytorch/fairseq/examples/MMPT.
BibTeX
@inproceedings{xu-etal-2021-videoclip,
title = "{V}ideo{CLIP}: Contrastive Pre-training for Zero-shot Video-Text Understanding",
author = "Xu, Hu and
Ghosh, Gargi and
Huang, Po-Yao and
Okhonko, Dmytro and
Aghajanyan, Armen and
Metze, Florian and
Zettlemoyer, Luke and
Feichtenhofer, Christoph",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.544/",
doi = "10.18653/v1/2021.emnlp-main.544",
pages = "6787--6800"
}