QUEMAT: QUEry-adaptive Media Asset Tracking

FOCAL International（FOCAL）是全球领先的贸易协会，促进在所有形式的媒体制作中使用商业镜头和其他内容，拥有300多个国际成员，包括内容库、档案馆、制作研究人员和服务提供商。多年来，该公司一直提供一种受欢迎的“视频广告”服务，产生销售线索并将其发送给会员。QUEMAT项目创建了新的深度学习技术（通过技术合作伙伴Dithen和The Media Institute），以创新一种具有高影响力的新“Footage Recommendation”服务，利用FOCAL International的既定足迹，并通过采用产生丰富的学习和训练数据，以不断提高项目的深度学习性能。QUEMAT可持续生态系统在项目内部和外部提供“网络效应”，在Footage Search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。该项目的时间安排非常理想，因为在三个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，开辟了新的市场途径（例如，Netflix现在比CBS在原创制作方面投入更多）; B）深度神经网络（DNN）技术已经达到允许对机器学习的投资提供前所未有的回报的成熟水平;以及c）执行“视频签名提取”的技术（即，允许快速搜索视频，而无需处理像素）已通过新的MPEG标准活动：“压缩视频分析描述符”（CDVA）验证，为广泛采用奠定了基础。该项目将创新新型DNN（深度神经网络）的媒体资产处理和发现功能，具有以下独特功能：i）它将允许每种查询类型的紧凑签名的连续产生，同时保持标准兼容性; ii）它将被专门训练以重新用于各种查询类型，具有可以离线执行的自动化查询驱动的定制阶段;以及iii）操作复杂性将是可调的，并且整个软件管道将可移植到任何公共云提供商，以便于跨媒体企业进行扩展和采用。总体而言，QUEMAT技术将为内容所有者带来更多的收入，并在内容制作者和素材库努力重新配置传统价值链以从中受益时提高运营效率。新的赚钱机会。该项目的三个合作伙伴：FOCAL、The Media Institute和Dithen --汇集了媒体行业的影响力和深度学习和媒体处理方面的领先技术专长，以及成功的合作记录。

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.