ULAD2022 - Daniel Kondermann (Quality Match GmbH) скачать в хорошем качестве

ULAD2022 - Daniel Kondermann (Quality Match GmbH)

Talk title: Dataset quality assurance via large scale human annotation Presenter: Daniel Kondermann (Quality Match GmbH), https://www.quality-match.com/ Recorded on Sunday, June 5, 2022, in Aachen, Germany. The 3rd workshop on Unsupervised Learning for Automated Driving (ULAD) at the IEEE Intelligent Vehicles Symposium 2022. http://intelligent-vehicles.org/ulad-... Abstract. I will review the research topics I have addressed during my times at Heidelberg University, focusing on ground truth generation. I will then discuss challenges in crowdsourcing and related large scale data annotation approaches, including a number of academically published use cases which aren’t immediately obvious. I’ll will discuss a list of abstract dataset quality metrics I’ve backronymed “RAD” which can be used to design new academic as well as industrial datasets with visual content. Based on examples, I will go through a number of concrete quality metrics and explain processes, tools and research opportunities to obtain the necessary raw data for dataset quality evaluation. Dr. Daniel Kondermann received his PhD (2009) and Habilitation (2016) at the Heidelberg Collaboratory for Image Processing. His research revolves around performance analysis of computer vision and machine learning methods, with a focus on dataset architecture, ranging from sensor array design to data annotation and performance metric definition. In 2013, he founded Pallas Ludens, a visual data annotation company. He and his team joined Apple in 2016, where he headed a small team researching dataset quality metrics for large scale annotation projects. Daniel left Apple in 2019 to create a new company. Quality Match is a visual dataset quality assurance company which is based on the hypothesis that the amount of data needed for machine learning is less important than its quality.