Related papers: Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

URL: http://arxiv.org/abs/2401.16076v1
Date: Mon, 29 Jan 2024 11:34:36 GMT
Title: Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas
Authors: Carlo Bretti, Pascal Mettes, Hendrik Vincent Koops, Daan Odijk, Nanne van Noord
Abstract summary: We introduce a multi-modal method for predicting the trailerness to assist editors in selecting trailer- worthy moments from long-form videos. We present results on a newly introduced soap opera dataset, demonstrating that predicting trailerness is a challenging task.
Score: 17.476344577463525
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a chal- lenging and time-consuming task. This requires selecting moments based on both visual and dialogue information. We introduce a multi-modal method for predicting the trailerness to assist editors in selecting trailer- worthy moments from long-form videos. We present results on a newly introduced soap opera dataset, demonstrating that predicting trailerness is a challenging task that benefits from multi-modal information. Code is available at https://github.com/carlobretti/cliffhanger

Related papers

MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation [62.85764872989189]
There is no publicly available dataset tailored for the analysis, evaluation, and training of long video generation models. We present MovieBench: A Hierarchical Movie-Level dataset for Long Video Generation. The dataset will be public and continuously maintained, aiming to advance the field of long video generation.
arXiv Detail & Related papers (2024-11-22T10:25:08Z)
TeaserGen: Generating Teasers for Long Documentaries [59.8220642722399]
We present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers. We propose a new two-stage system for generating teasers from long documentaries.
arXiv Detail & Related papers (2024-10-08T01:00:09Z)
MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions [69.9122231800796]
We present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions. We propose a systemic captioning framework, achieving various modality annotations with more than 27.1k hours of trailer videos. Our dataset potentially paves the path for fine-grained large multimodal-language model training.
arXiv Detail & Related papers (2024-07-30T16:43:24Z)
Towards Automated Movie Trailer Generation [98.9854474456265]
We introduce Trailer Generation Transformer (TGT), a deep-learning framework utilizing an encoder-decoder architecture. TGT movie encoder is tasked with contextualizing each movie shot representation via self-attention, while the autoregressive trailer decoder predicts the feature representation of the next trailer shot. Our TGT significantly outperforms previous methods on a comprehensive suite of metrics.
arXiv Detail & Related papers (2024-04-04T14:28:34Z)
Film Trailer Generation via Task Decomposition [65.16768855902268]
We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches.
arXiv Detail & Related papers (2021-11-16T20:50:52Z)
A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers [15.889598494755646]
We introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube. We propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem.
arXiv Detail & Related papers (2021-01-26T17:15:35Z)
Learning Trailer Moments in Full-Length Movies [49.74693903050302]
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. We construct the first movie-trailer dataset, and the proposed Co-Attention assisted ranking network shows superior performance even over the supervised approach.
arXiv Detail & Related papers (2020-08-19T15:23:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.