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

Abstract

In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly, we define a self-supervised learning framework that captures the cross-modal information. A novel adversarial learning module is then introduced to explicitly handle the noises in the natural videos, where the subtitle sentences are not guaranteed to be strongly corresponded to the video snippets. For training and evaluation, we contribute a new dataset `ApartmenTour' that contains a large number of online videos and subtitles. We carry out experiments on the bidirectional retrieval tasks between sentences and videos, and the results demonstrate that our proposed model achieves the state-of-the-art performance on both retrieval tasks and exceeds several strong baselines. The dataset can be downloaded at https://github.com/zyj-13/WAL.

arXiv commentNeurIPS 2020 Self-Supervised Learning Workshop

References in corpus (5)

• Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
• HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
• Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
• Explain Images with Multimodal Recurrent Neural Networks
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