2018/10/18 by Cheng-Kang Hsieh, Hsieh, Cheng-Kang, Miguel Del Campo +11
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Feature (linguistics) #Filter (signal processing) #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Object (grammar) #Pooling #Recommender Systems and Techniques #Trailer #Video Analysis and Summarization #cs.AI #cs.CV #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1810.08189
8 pages, 3 figures, 1 table include ablation study. arguments / results unchanged
openalex publication_date 2018/10/18 · arxiv created 2018/10/22 · arxiv updated 2018/10/24 · openalex created_date 2018/10/26 · openalex updated_date 2026/07/28
This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.