Our objective in this work is long range understanding of the narrative structure of movies. Instead of considering the entire movie, we propose to learn from the `key scenes' of the movie, providing a condensed look at the full storyline. To this end, we make the following three contributions: (i) We create the Condensed Movies Dataset (CMD) consisting of the key scenes from over 3K movies: each key scene is accompanied by a high level semantic description of the scene, character face-tracks, and metadata about the movie. The dataset is scalable, obtained automatically from YouTube, and is freely available for anybody to download and use. It is also an order of magnitude larger than existing movie datasets in the number of movies; (ii) We provide a deep network baseline for text-to-video retrieval on our dataset, combining character, speech and visual cues into a single video embedding; and finally (iii) We demonstrate how the addition of context from other video clips improves retrieval performance.
@article{arxiv.2005.04208,
title = {Condensed Movies: Story Based Retrieval with Contextual Embeddings},
author = {Max Bain and Arsha Nagrani and Andrew Brown and Andrew Zisserman},
journal= {arXiv preprint arXiv:2005.04208},
year = {2020}
}
Comments
Appears in: Asian Conference on Computer Vision 2020 (ACCV 2020) - Oral presentation