Elisa Ricci is an Associate Professor at the University of Trento and a Researcher at Fondazione Bruno Kessler, Italy. She received her PhD degree in Electrical Engineering from the University of Perugia in 2008. Her main research interests are directed along developing deep learning algorithms for human behaviour analysis from visual and multi-modal data. She received the ACM Multimedia 2015 best paper award, the IBM Best Student Paper award at ICPR 2014 and the INTEL Best Paper award at ICPR 2016. She is associate editor of IEEE Transactions on Multimedia and ACM Transactions on Multimedia Computing, Communications, and Applications. She is/was Area Chair of ACM MM 2016-2019, ECCV 2016, ICCV 2017, BMVC 2018-2019 and Program Chair of ACM MM 2020 and ICIAP 2019.
Deep networks have significantly improved the state of the arts for several tasks in computer vision and robotic perception. Unfortunately, the impressive performance gains have come at the price ofause of massive amounts of labeled data. As the cost of collecting and annotating data is often prohibitive, given a target task where few or no training samples are available, it would be desirable to build effective learners that can leverage information from labeled data of a different but related source domain. However, a major obstacle in adapting models to the target task is the shift in data distributions across domains. This problem, typically referred as domain shift, has motivated research into Domain Adaptation (DA).
Traditional DA algorithms assume the presence of a single source and a single target domain. However, in real-world applications different situations may arise. For instance, in some cases multiple datasets from diverse source domains may be available, while in other settings target samples may not be given at the training stage or may arisefrom temporal data streams. Alternatively, in some applications knowledge about different domains may only be provided in form of side-information (e.g. metadata, captions) and should be effectively exploited to guide the adaptation process. In this talk I will provide an overview of the problem of DA, focusing onvisual recognition and robot perception tasks, and describe recent works on adaptationin case ofdynamic, real-world settings.