QUEMAT: QUEry-adaptive Media Asset Tracking

Focus International(FOCUS)是全球领先的行业协会，促进在所有形式的媒体制作中使用商业素材和其他内容，拥有300多名国际成员，包括内容图书馆、档案馆、制作研究人员和服务提供商。该公司多年来一直提供广受欢迎的“镜头搜索器”服务，产生销售线索，并将这些线索发送给会员。QUEMAT项目创造了新的深度学习技术(通过技术合作伙伴Diten和媒体研究所)，以创新高影响力的新“镜头搜索器”服务，利用Focus International的既定足迹，进而通过采用产生大量的学习和培训数据，以不断提高项目的深度学习绩效。QUEMAT的可持续生态系统在项目内外提供“网络效应”，在镜头查找器内部和大量应用程序中为视觉搜索查询提供高精度和商业相关的结果。该项目的时机非常理想，因为三个完全不同的领域同时达到了拐点：a)电影和电视制作正在蓬勃发展，出现了新的进入市场的途径(例如，Netflix现在对原创制作的投资比CBS更多)；b)深度神经网络(DNN)技术已经达到成熟的水平，允许对机器学习的投资带来前所未有的回报；和c)执行“视频签名提取”(即，允许在不需要处理像素的情况下快速搜索视频)的技术已通过新的mpeg标准活动验证：“视频分析的紧凑描述符”(CDVA)，为广泛采用奠定了基础。该项目将创新基于DNN(深度神经网络)的媒体资产处理和发现能力，具有以下独特特征：i)它将允许每种查询类型连续产生紧凑签名，同时保持符合标准；Ii)QUEMAT技术将经过专门培训，以便通过可离线执行的自动化查询驱动定制阶段重新调整各种查询类型的用途；iii)运营复杂性将是可调的，整个软件管道将可移植到任何公共云提供商，以便在媒体企业之间轻松扩展和采用。总体而言，在内容生产商和素材库努力重新配置传统价值链以从新的盈利机会中获益之际，QUEMAT技术将为内容所有者带来更多收入并提高运营效率。该项目的三个合作伙伴：Focus、媒体研究所和Diten--将媒体行业的存在和深度学习和媒体处理方面的领先技术专业知识以及成功的合作记录结合在一起。

FOCAL International (FOCAL) is the leading global trade association facilitating use of commercial footage and other content in all forms of media production, with over 300 international members comprising content libraries, archives, production researchers and service providers. The company has offered a popular "Footage Finder" service for many years, generating sales leads and routing these to members. The QUEMAT project creates novel deep learning technology (via technology partners Dithen and The Media Institute) to innovate a high-impact new "Footage Finder" service, leveraging the established footprint of FOCAL International, and in turn generating an abundance of learning and training data through adoption to continuously improve the project's deep learning performance. The QUEMAT sustainable ecosystem delivers 'network effects' inside and outside the project, yielding high-precision and commercially relevant results to visual search queries within Footage Finder and across an abundance of applications.The project is ideally timed as inflection points have been reached concurrently in three disparate arenas: a) film and television production is burgeoning with new routes to market (e.g. Netflix now invest more in original production than CBS); b) deep neural network (DNN) technology has reached a level of maturity allowing investments in machine learning to deliver unprecedented returns; and c) technology to perform 'video signature extraction' (i.e., allowing for rapid search of video without the need to process pixels) has been validated by new MPEG standards activity: 'Compact Descriptors for Video Analysis' (CDVA), creating a foundation for widespread adoption.The project will innovate the novel DNN (Deep Neural Network) -based media asset processing and discovery capabilities with the following unique features: i) it will allow for the continuous production of compact signatures per query type while remaining standards-compliant; ii) it will be specifically trained to be repurposed for various query types with an automated query-driven customization stage that can be performed offline; and iii) the operational complexity will be tunable and the entire software pipeline will be portable to any public cloud provider for easy scaling and adoption across media enterprises.Overall, QUEMAT technology will deliver increased revenues to content owners and increase operational efficiencies at a time when content producers and footage libraries are struggling to reconfigure traditional value chains to benefit from new monetization opportunities. The project's three partners: FOCAL, The Media Institute and Dithen -- bringing together media industry presence and leading technology expertise in deep learning and media processing, and a record of successful collaboration.