May 8, 2020 · 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 ...

Our technique involves the creation of a character embedding bank (CEB) which contains a list of char- acters (obtained from cast lists), and a corresponding ...

This repository contains the video dataset, implementation and baselines from Condensed Movies: Story Based Retrieval with Contextual Embeddings.

Feb 26, 2021 · CBM receives contextual video features (which are previous clips from the same movie) to improve the multimodal encoding of the target video ...

Each movie is condensed into approximately 20 minutes of footage, providing efficient video stories. 400K+. facetracks. Nearly half a million facetracks from ...

The Condensed Movie Dataset (CMD) is created, consisting of the key scenes from over 3K movies: each key scene is accompanied by a high level semantic ...

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 ...

Introduced by Bain et al. in Condensed Movies: Story Based Retrieval with Contextual Embeddings. A large-scale video dataset, featuring clips from movies with ...

May 8, 2020 · Our objective in this work is the long range understanding of the narrative structure of movies. Instead of considering the entire movie, ...

Condensed Movies: Story Based Retrieval with Contextual Embeddings. Max Bain, Arsha Nagrani, Andrew Brown, Andrew Zisserman. Page 2. Motivation. • Long term ...