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In this paper, we teach machines to understand visuals and natural languageby learning the mapping between sentences and noisy video snippets withoutexplicit annotations. Firstly, we define a self-supervised learning frameworkthat captures the cross-modal information. A novel adversarial learning moduleis then introduced to explicitly handle the noises in the natural videos, wherethe subtitle sentences are not guaranteed to be strongly corresponded to thevideo snippets. For training and evaluation, we contribute a new dataset`ApartmenTour' that contains a large number of online videos and subtitles. Wecarry out experiments on the bidirectional retrieval tasks between sentencesand videos, and the results demonstrate that our proposed model achieves thestate-of-the-art performance on both retrieval tasks and exceeds several strongbaselines. The dataset can be downloaded at https://github.com/zyj-13/WAL.

Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision