Christoph Feichtenhofer


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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
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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

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VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
Hu Xu | Gargi Ghosh | Po-Yao Huang | Prahal Arora | Masoumeh Aminzadeh | Christoph Feichtenhofer | Florian Metze | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021