Proceedings of the 2nd International Workshop on AI for Smart TV Content Production, Access and Delivery最新文献

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AI in the Media Spotlight 媒体聚光灯下的人工智能
A. Rouxel
{"title":"AI in the Media Spotlight","authors":"A. Rouxel","doi":"10.1145/3422839.3423059","DOIUrl":"https://doi.org/10.1145/3422839.3423059","url":null,"abstract":"The use of AI technology offers many new opportunities for the media sector; in particular, it leads to an increase in productivity and efficiency to convey relevant information to appropriate viewers quickly and accurately. In this keynote, I will show how AI is gradually transforming the content production and distribution chain for broadcasters and media in general. We will start with an overview of AI applications in the media field and the underlying technologies. Then we will go through some projects led or developed by the EBU for the Public Media Services, PSM. To conclude, I will sketch the trend, the limitations and potential evolutions of the uptake of AI in media. The range of AI applications in the mediums of written press, cinema, radio, television and advertising is widespread. To start with the content production and post-production AI is used in video creation and editing, in the written press, for automatic or assisted writing, information analysis and verification. Without being exhaustive, in the broad field of audience analytics, AI can identify the optimal audience for a given content, personalise and recommend the content for a targeted audience or specific user depending on the granularity. From the perspective of accessibility and inclusion, AI plays a predominant role in improving access to content through transcription, translation, vocal synthesis and recommendation. In this context of the raising of AI in the media sphere, the PSM are facing the need to be innovative and transform their value chain to reach the audience better. This can't be performed without keeping the PSM remit which combines a full range of distinctive quality content to fulfil its central mission: inform, educate, entertain. As such, the EBU is leading projects and developing technologies to leverage AI capabilities for media while meeting PSM remit [1]. Firstly, the EBU is leading a project to benchmark AI tools on the market in the context of PSM. As a first step, we are focusing on Automatic Speech Recognition. Therefore I will describe the objective, the metrics and the evolution of the tool. Among other activities related to machine learning and metadata, the EBU is developing a tool to generate high-level tags on written content. Since NLP recently achieved a breakthrough for many applications, we are working on leveraging this technology to produce high-level explainable tags on written contents. As they are called high level, these tags identify properties correlated with several groups of linguistic features like vocabulary, grammar, semantic or formality. Originally designed to detect fake news they can as well feed recommender systems or classifiers. I will detail the machine learning algorithms behind the tool and pave the way of future works.","PeriodicalId":270338,"journal":{"name":"Proceedings of the 2nd International Workshop on AI for Smart TV Content Production, Access and Delivery","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124271435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework 通过VISIOCITY进行现实视频总结:一个新的基准和评估框架
Vishal Kaushal, S. Kothawade, Rishabh K. Iyer, Ganesh Ramakrishnan
{"title":"Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework","authors":"Vishal Kaushal, S. Kothawade, Rishabh K. Iyer, Ganesh Ramakrishnan","doi":"10.1145/3422839.3423064","DOIUrl":"https://doi.org/10.1145/3422839.3423064","url":null,"abstract":"Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking dataset called VISIOCITY which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and other vision problems. Secondly, for long videos, human reference summaries, necessary for supervised video summarization techniques, are difficult to obtain. We present a novel recipe based on pareto optimality to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea. We propose a simple recipe VISIOCITY-SUM to enhance an existing model using a combination of losses and demonstrate that it beats the current state of the art techniques. We also present a study of different desired characteristics of a good summary and demonstrate that a single measure (say F1) to evaluate a summary, as is the current typical practice, falls short in some ways. We propose an evaluation framework for better quantitative assessment of summary quality which is closer to human judgment than a single measure. We report the performance of a few representative techniques of video summarization on VISIOCITY assessed using various measures and bring out the limitation of the techniques and/or the assessment mechanism in modeling human judgment and demonstrate the effectiveness of our evaluation framework in doing so.","PeriodicalId":270338,"journal":{"name":"Proceedings of the 2nd International Workshop on AI for Smart TV Content Production, Access and Delivery","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126609049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
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