Special Report, Americas, Travel

[AbertBic]

Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Cross-modal retrieval, video-text retrieval, video representation learning, preview-aware attention. With the increasing popularity of video streaming platforms such as YouTube and TikTok, there has been an explosive growth of video data on the Internet. In this paper, we focus on text-to-video retrieval. Given a query in the form of a natural-language sentence, the task is to retrieve videos semantically relevant to the given query from many unlabeled videos. For building such a video retrieval model, how to compute the semantic similarity between two modalities, i.e., text and videos, is crucial. Following the line of cross-modal representation learning based method, in this work we concentrate on video representation learning, an essential component for text-to-video retrieval.




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