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• W3213782737 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/tree/main/examples/MMPT." @default.
• W3213782737 created "2021-11-22" @default.
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• W3213782737 date "2021-09-28" @default.
• W3213782737 modified "2023-10-17" @default.
• W3213782737 title "VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding" @default.
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• W3213782737 doi "https://doi.org/10.48550/arxiv.2109.14084" @default.
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