Prof. Nicu. Sebe (Univ. of Trento, Italy)
Title: Deep Learning for Analysis and Generation of Facial Attributes.
Abstract: Face analysis is a fundamental and challenging problem in computer vision. For face analysis, one of the fundamental techniques is face alignment which is usually employed as the preprocessing step. For instance, when we do face verification, the faces need to be aligned first. On top of that, face pose and facial expression normalization is also a very important preprocessing step. In addition to the disturbance of the poses and expressions, another very important factor that might degrade the performance of face verification is age. Thus, modeling the aging process of human faces is important for cross-age face verification and recognition. Similar to face verification, a face aging task also needs to normalize the face poses and expressions, and many works have been proposed to normalize for facial expressions. However, only a few works have studied how to generate faces having various expressions, such as spontaneous or posed smiles. In this presentation, we will present our recent results on face alignment, face aging, and smile video generation employing recurrent neural networks, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU).
Short Bio: Nicu Sebe is a professor in the University Of Trento, Italy, where he is the director of the Department of Information Engineering and Computer Science. He is leading the research in the areas of multimedia information retrieval and human-computer interaction in computer vision applications. He was involved in the organization of the major conferences and workshops addressing the computer vision and human-centered aspects of multimedia information retrieval, among which as a General Co-Chair of the IEEE Automatic Face and Gesture Recognition Conference, FG 2008, ACM International Conference on Image and Video Retrieval (CIVR) 2007 and 2010. He was a general chair of ACM Multimedia 2013 and ACM ICMR 2017 and a program chair of ACM Multimedia 2011 and 2007, ECCV 2016 and ICCV 2017. He is the program chair of ICPR 2020. Currently he is the ACM SIGMM vice chair. He is a fellow of IAPR and a Senior member of ACM and IEEE.
Prof. Ricardo Chavarriaga (EPFL STI IBI-STI CNBI Switzerland )
Title: Symbiotic interaction through brain-machine interfacing, machine learning and VR.
Abstract: (to be published)
Short Bio: Ricardo Chavarriaga is a senior researcher at the Center of Neuroprosthetics of the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a PhD in computational neuroscience and has more than 100 scientific publications on brain-machine interfacing and neurotechnologies. His work focuses on understanding and decoding the neural mechanisms that govern cognitive processes such as error awareness, attention and decision making. His vision targets a future where this information can be used to endow artificial devices with the capability to adapt to their user’s goals, preferences and capabilities so as to establish a truly symbiotic relation between human and machines.
Website: http://people.epfl.ch/ricardo.chavarriaga. Twitter: @Chavarriaga_BCI
Prof. Jordi González (CVC-UAB, Spain)
Title: Going beyond Deep Learning for Understanding Human Behaviours in Image Sequences.
Abstract: (to be published)
Short Bio: Jordi Gonzàlez received the Ph.D. degree in computer engineering from Universitat Autònoma de Barcelona (UAB) in 2004. He is at present associate professor in computer science at the Computer Science Department, UAB. He is also senior researcher at the Computer Vision Center, where he has co-founded three spin-offs (Cloud Size Services, Visual Tagging, and Care Respite) and the Image Sequence Evaluation (ISE Lab) research group. His research interests include machine learning techniques for the computational interpretation of human behaviour in social images, or Visual Hermeneutics.