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In this work, we present Auto-captions on GIF, which is a new large-scalepre-training dataset for generic video understanding. All video-sentence pairsare created by automatically extracting and filtering video caption annotationsfrom billions of web pages. Auto-captions on GIF dataset can be utilized topre-train the generic feature representation or encoder-decoder structure forvideo captioning, and other downstream tasks (e.g., sentence localization invideos, video question answering, etc.) as well. We present a detailed analysisof Auto-captions on GIF dataset in comparison to existing video-sentencedatasets. We also provide an evaluation of a Transformer-based encoder-decoderstructure for vision-language pre-training, which is further adapted to videocaptioning downstream task and yields the compelling generalizability onMSR-VTT. The dataset is available at\url{http://www.auto-video-captions.top/2020/dataset}.

Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training