EyCon (Visual AI and Early Conflict Photography)
EyCon (Visual AI and Early Conflict Photography)
批准号:
AH/W008408/1
负责人:
Lise Jaillant
金额:
$12.88万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --
中文摘要
随着越来越多的博物馆和档案馆将他们的图像收藏数字化,需要新的知识形式和对这些越来越多的过去照片的探索。计算机技术可以处理数以万计的图像并帮助将其可视化,这将是未来解锁这些数字档案的关键。该项目的主要成果之一是创建了一个创新的战时摄影数据库,其中将包括以前未出版的材料。EyCon专注于早期冲突摄影(1890-1918),记录从经常被忽视的殖民运动到第一次世界大战的大规模武装暴力。它被设计为数千张原始照片和打印图像的集合:尽管规模很大,但由于计算机技术,这些集合将被发现。EyCon的任务之一是提高对图像中对象的识别,这些对象与当前模型训练的照片截然不同。EyCon还将提供和应用工具,以确定当代印刷品中照片和印刷图像之间的相似之处,以便追溯图像的流通。还将开发和测试整个语料库的各种可视化,以帮助用户以一种有益的方式浏览非常大的图像集合。早期战时图像的大规模发现引发了广泛的伦理和方法问题,EyCon将直接解决这些问题。首先,EyCon将开发解决方案,以克服人工智能技术应用于潜在有争议的视觉遗产时的偏见和不准确性。计算方法并不是中立的。它们可能复制现有的权力结构,也可能无法解开过去不平衡的关系。EyCon将非常小心地强调远见的好处和局限性。其次,战时照片可能是非常敏感的材料,特别是在以种族和性别暴力为特征的殖民和帝国背景下。以一种合理的方式提供和发现这些图片,可能会为多元化的叙事和视角开辟道路,这是该项目的核心。第三,虽然EyCon将帮助恢复数千张图像中几个冲突的可见性,但它也必须考虑摄像机没有记录下来的、可以在大量数字化视觉材料的重量下粉碎的缺席图像、暴力和冲突的形式。EyCon项目将产生以下研究成果:_2个与项目合作伙伴合作组织的研讨会_2个论文集(期刊特刊或编辑过的卷)_1个集成了所有人工智能功能的演示网站和一个元搜索引擎,以探索Huma-num和合作机构的服务器上保存的数据。该网站将同时面向专业和非专业最终用户。它将被设想为一个用户友好的工具,无论是否有技术专长的人都可以使用。它将允许我们为那些想要探索整个Eycon语料库的用户提供更强的可发现性和智能可视化。_一个开放源代码库(GitLab),它将使EyCon的脚本和其他工具公开地提供给广泛的社区。该存储库将有助于数据科学家和开发人员重用该项目产生的脚本和数据。这将确保项目代码在其他项目中可复制和使用。档案当然不是学术研究人员的专属。演示网站将促进公众参与早期冲突摄影和应用于摄影史的计算方法这一主题。相关社交媒体和专门的Eycon列表服务器将帮助我们联系学术界、档案机构和其他领域的感兴趣的各方。
英文摘要
As more and more museums and archives are making their image collections digitally available, new forms of knowledge and exploration of this growing mass of pictures from the past are needed. Computational techniques, which can process and help visualise tens of thousands of images, will be key to unlocking these digital archives in the future. One of the main achievements of the project is to create an innovative database on wartime photography that will include previously unpublished material. EyCon focuses on early conflict photography (1890-1918) documenting mass armed violence, from often overlooked colonial campaigns to the First World War. It is designed as a collection of thousands of original photographs and printed images: despite its scale, this collection will be made discoverable thanks to computational techniques. One of EyCon's tasks is to improve the recognition of objects in images that are very different from the photographs current models have trained on. EyCon will also provide and apply tools to identify similarities between photographs and printed images in contemporary prints in order to retrace the circulation of images. Various visualisations of the entire corpus will also be developed and tested to help users navigate a very large collection of images in a rewarding way. The large-scale discovery of early wartime imagery raises a wide range of ethical and methodological issues that EyCon will directly address. First, EyCon will develop solutions to overcome bias and inaccuracies when AI techniques are applied to a potentially contested visual heritage. Computational methods are not neutral. They may reproduce existing power structures or fail to unravel the unbalanced relationships of the past. EyCon will take great care in emphasising both the benefits and limitations of distant vision. Second, wartime photographs can be very sensitive material, especifically in colonial and imperial contexts that are characterised by racial and gendered violence. Making these pictures available and discoverable in a sensible way that might open the way for a multiplicity of narratives and perspectives is central to the project. Third, while EyCon will help restore the visibility of several conflicts in thousands of images, it also has to consider absent images, forms of violences and conflicts that were not recorded by cameras and that can be crushed under the weight of a large mass of digitised visual material.The EyCon project will lead to the following research outputs: _2 workshops organised in collaboration with Project Partners_2 collections of essays (special issues of journals or edited volumes)_1 demo website integrating all AI functionalities and a metasearch engine to explore data held on Huma-Num and partner institutions' servers. This website will be designed for both specialist and non-specialist end-users. It will be conceived as a user-friendly tool for everyone with or without technical expertise. It will allow us to offer increased discoverability and smart visualisations to users who want to explore the entire Eycon corpus._An open-source repository (Gitlab) that will make EyCon's scripts and other tools openly available to a broad community. This repository will be useful for data scientists and developers to reuse the scripts and data produced by the project. This will ensure that the project code is replicable and usable in other projects.Archives are of course not reserved to academic researchers. The demo website will foster public engagement on the topic of early conflict photography and computational methods applied to the history of photography. Associated social media and a dedicated Eycon list-serv will help us connect with interested parties - in academia, archival institutions and beyond.
期刊论文(7)
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(Mis)Matching Metadata: Improving Accessibility in Digital Visual Archives through the EyCon Project
DOI:
10.1145/3594726
发表时间:
2023-05
期刊:
ACM Journal on Computing and Cultural Heritage
影响因子:
2.4
作者:
[Katherine Aske;Marina Giardinetti]
通讯作者:
Katherine Aske;Marina Giardinetti
DOI:
10.3917/sr.055.0227
发表时间:
2023
期刊:
Sociétés & Représentations
影响因子:
--
作者:
[Foliard D]
通讯作者:
Foliard D
More Data, Less Process: A User-Centered Approach to Email and Born-Digital Archives
更多数据,更少流程:以用户为中心的电子邮件和原生数字档案方法
DOI:
10.17723/2327-9702-85.2.533
发表时间:
2022
期刊:
The American Archivist
影响因子:
--
作者:
[Jaillant L]
通讯作者:
Jaillant L
Application of pose recognition to Valois albums of the First World War: Some Artificial Intelligence Tracks for the History of Photography
姿势识别在第一次世界大战瓦卢瓦相册中的应用:摄影史的一些人工智能轨迹
DOI:
--
发表时间:
2023
期刊:
Matériaux pour l'histoire de notre temps
影响因子:
--
作者:
[Dentler J]
通讯作者:
Dentler J
DOI:
10.3917/sr.055.0055
发表时间:
2023
期刊:
Sociétés & Représentations
影响因子:
--
作者:
[Danguy L]
通讯作者:
Danguy L
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资助金额:$10.28万
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负责人:Lise Jaillant
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负责人:Lise Jaillant
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基于多幅图象的Visual Hull重构及表面属性建模算法研究
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