Big Data in Culture, Design and Heritage 2019
1st International Workshop on Big Data in Culture, Design and Heritage
The workshop will address applications of Big Data in digital culture, design, and heritage. The conceptual premise of the workshop is based on an emerging and increasingly significant field of interdisciplinary inquiry popularly known as Digital Humanities, which examines use and application of digital technologies in humanities, the liberal arts, social science scholarship, and beyond. However, while the use of computational tools in social science and humanities work is not very new, the availability of a large body of cultural artifacts after the digital turn, as well as emergence of new kinds of digital objects and embodiments, has opened up several possibilities for social science and humanities research, practice and pedagogy using computational approaches. This workshop aims to capture this emerging moment and focus on one unavoidable aspect of ‘digital’ research in culture, design, and heritage: Big Data. As is commonly understood, the nature of ‘bigness’ in Big Data is not one of size, but because of its nature as networked data, often exemplified by intricate systems of rich information from heterogeneous data sources such as Social Networks, Digital Libraries, and Multimedia Collections and Archives, etc. which influence and often enhance users’ experience. The workshop aims to highlight challenges and opportunities that such large scale multimodal data analytics brings to the community in these areas. The workshop will bring together researchers from the qualitative and quantitative domains on a common platform and facilitate in answering key interdisciplinary research questions.
CM-PRID: Cross Modal Person Reidentification 2019
1st Internation Workshop on Cross Modal Person Reidentification
Person reidentification has received a lot of attention in the recent past due to its potential in visual surveillance. Most of the works focus on visible spectrum and use single modality. With widespread surveillance and more stringent constraints, the current requirement is to develop techniques which can address the cross modality nature of the captured data. In addition to the visible spectrum reidentification challenges such as pose, illumination variation, scale variation, and occlusion, cross-modal dataset also pose the challenge of domain or spectrum variation. Thus, the cross-modal reidentification becomes practically very challenging. This necessitates two goals which are the focus of the workshop. First, to generate cross-modal datasets such as text-image, RGB-IR, image-video and RGB-Depth datasets. Second, novel techniques which can bridge the domain gap between the two modalities. Though some preliminary datasets and techniques exist, there is a huge scope of contribution towards the two goals.
MLIAU: Machine Learning for Image analysis and Understanding 2019
1st International Workshop on Machine Learning for Image analysis and Understanding 2019
Recently, image contents collected from surveillance cameras, mobile phones, personal photo collections, news footage, or medical images have been explosively increased. How to automatically/quantitatively analyze and understand the acquired image contents is becoming one of the most active research areas in the vision community due to the scientifically challenging problems and its great benefits to real life applications. On the other hand, machine learning techniques especially the deep learning framework have manifested the surprising superiority for extracting structural and inherent representation in numerous computer vision applications such as image classification, object detection/localization, image segmentation, captioning, and so on. With machine learning techniques, it is prospected to discover the inherent structure of the available unconditioned visual contents and to achieve more promising results for various applications based on image analysis. This workshop, on Machine Learning for Image Analysis and Understanding (MLIAU2019) – aims at sharing latest progress and developments, current challenges, and potential applications for exploiting large amounts of image contents. We are interested in constructing effective systems to enable image analysis/understanding and building wide applications within the fields of artificial intelligence, machine learning, image processing, data mining, and others.