Learning Trailer Moments in Full-Length Movies with Co-Contrastive Attention
| Autor: | Lezi Wang, Dimitris N. Metaxas, Rohit Puri, Dong Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Contrast (statistics) 02 engineering and technology Construct (python library) 010501 environmental sciences computer.software_genre 01 natural sciences Term (time) Ranking 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Key (cryptography) Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence Performance improvement business computer ComputingMilieux_MISCELLANEOUS Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585228 ECCV (18) |
| DOI: | 10.1007/978-3-030-58523-5_18 |
| Popis: | A movie’s key moments stand out of the screenplay to grab an audience’s attention and make movie browsing efficient. But a lack of annotations makes the existing approaches not applicable to movie key moment detection. To get rid of human annotations, we leverage the officially-released trailers as the weak supervision to learn a model that can detect the key moments from full-length movies. We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to be scored higher than the uncorrelated moments. Additionally, we propose a Contrastive Attention module to enhance the feature representations such that the comparative contrast between features of the key and non-key moments are maximized. We construct the first movie-trailer dataset, and the proposed Co-Attention assisted ranking network shows superior performance even over the supervised(The term “supervised” refers to the approach with access to the manual ground-truth annotations for training.) approach. The effectiveness of our Contrastive Attention module is also demonstrated by the performance improvement over the state-of-the-art on the public benchmarks. |
| Databáze: | OpenAIRE |
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