Saliency Detection in Audio-Visual Content Professors and Supervisors

Moncef Gabboujمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.

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Helmut Grabnerمشاهده پروفایل

استاد

Zurich University of Applied Sciences (ZHAW)Switzerland

Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.

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Moncef Gabboujمشاهده پروفایل

استاد

Tampere UniversityFinland

Moncef Gabbouj is a Professor of Signal Processing at the Department of Computing Sciences, Tampere University, Finland. He holds a PhD from Purdue University and has held academic positions including Academy of Finland Professor (2011–2015) and Head of the Department of Signal Processing (2002–2007). His research focuses on artificial intelligence, machine learning, multimedia signal processing, and nonlinear signal/image processing. He has authored over 800 papers and supervised 64 doctoral and 72 master’s theses, earning accolades such as IEEE Fellow, Finnish Cultural Foundation Award, and TUT Foundation Grand Award. Education: BS (Electrical Engineering, Oklahoma State University, 1985), MS and PhD (Electrical Engineering, Purdue University, 1986–1989). Visiting roles include Hong Kong University of Science and Technology and University of Southern California. Research interests include Big Data analytics, multimedia content analysis, pattern recognition, and video coding. He leads the Artificial Intelligence Research Task Force of the Research Alliance on Autonomous Systems (RAAS) and directs the NSF IUCRC Center for Visual and Decision Informatics (CVDI). Awards highlight contributions to signal processing and AI, including IEEE Fourier Award Committee membership and leadership roles in EURASIP and IEEE. Grants and projects span EU Horizon programs, NSF, and industry collaborations.

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Charith Abhayaratneمشاهده پروفایل

مدرس ارشد

University of SheffieldUnited Kingdom

Dr. Charith Abhayaratne is a Senior Lecturer and EEE Foundation Year Tutor at the School of Electrical and Electronic Engineering, University of Sheffield. He leads the Communications Research Group and serves as the accreditation team lead for the school. With qualifications including a PhD from the University of Bath and a B.E. from the University of Adelaide, his research focuses on signal processing, machine learning, multimedia security, and video coding. His work explores blockchain for content protection, visual salience in robotics, human activity recognition, and advanced video coding techniques (HDR, UHD, 360° video). His research has been funded by Innovate UK, EPSRC, and industry partners. Education: B.E. (Electrical and Electronic Engineering), The University of Adelaide, Australia (1998) PhD (Electronic and Electrical Engineering), University of Bath, UK (2002) PGCertHE (Higher Education), University of Sheffield (2008) Fellow of the Higher Education Academy (FHEA), Member of the Institution of Engineering and Technology (MIET), Member of IEEE (MIEEE) Research Interests: His work spans multimedia security (data hiding, blockchain), computer vision (visual salience, object recognition), and video coding (HDR/UHD). Current projects include robotic vision applications, assisted living through activity recognition, and international standards development (JPEG/MPEG). He has contributed to scalable video standards and serves on technical committees for IEEE, EURASIP, and APSIPA. Awards & Service: Recipient of the Alain Bensoussan Fellowship (ERCIM, 2002) Associate Editor for IEEE Transactions on Image Processing, IEEE Access, and Elsevier JISA Member of EPSRC Peer Review College and British Standards Institution (BSI) Grants & Labs: Active grants from Innovate UK and EPSRC support projects in multimedia security and video coding. His lab leads interdisciplinary work in AI-driven visual analytics and secure media distribution frameworks.

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Nancy Zlatintsiمشاهده پروفایل

پژوهشگر

National Technical University of AthensGreece

Nancy Zlatintsi is a Postdoctoral Research Associate at the National Technical University of Athens (NTUA) in the Computational Vision and Signal Processing (CVSP) group at the School of Electrical and Computer Engineering. She earned her Diploma in Media Engineering from KTH Royal Institute of Technology (2006) and a Ph.D. in Audio and Multimedia Processing from NTUA (2013). Her research focuses on Music Information Retrieval (MIR) , audio signal processing , and multimodal interaction , with applications in human-robot interaction and movie summarization . Her work has been funded by the European Social Fund (Heracleitus II program) and she has contributed to European projects like iMuSciCA and e-Prevention . Her research spans multimodal saliency detection , audio event recognition , and computational models for emotion tracking . Key scientific contributions include publications in top venues such as IEEE ICASSP , CVPR , and EURASIP Journal on Image and Video Processing . She is also involved in assistive robotics and gerontechnology applications. Email: nzlat@cs.ntua.gr Office: 2.2.19, NTUA

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Jorma Laaksonenمشاهده پروفایل

مدرس ارشد

Aalto UniversityFinland

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

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Jianbo Jiaoمشاهده پروفایل

دانشیار

Brown UniversityUnited States

Jianbo Jiao is an Associate Professor in Computer Vision and Machine Learning at the School of Computer Science, University of Birmingham, where he leads the MIx (Machine Intelligence + x) research group. Previously, he was a Postdoctoral Researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford, working on the SeeBiByte and VisualAI projects. MSc from Peking University (2015) PhD in Computer Science from City University of Hong Kong (2018), supported by Hong Kong PhD Fellowship Scheme Visiting scholar at Beckman Institute, University of Illinois at Urbana-Champaign (2017-2018) Dr. Jiao's research focuses on Computer Vision, Machine Learning, Healthcare AI, and AI for Science. His work primarily investigates learning representations in open-world scenarios by exploring auxiliary information from multiple modalities (image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion) and reducing dependency on supervision through self-supervised, weakly-supervised, and semi-supervised learning approaches. His group has made significant contributions to multimodal learning, medical image analysis, and 3D vision. His recent publications demonstrate strong trends in multimodal learning, medical AI applications, and 3D computer vision. The research spans fundamental computer vision tasks like segmentation and scene understanding while addressing practical challenges in healthcare and scientific domains. Notably, there's significant focus on making vision systems more robust to distribution shifts and requiring less labeled data. Amazon Research Award recipient (2024) Royal Society Short Industry Fellow Best Paper Award at MICAD 2024 Best Paper Award at MICCAI 2024 ASMUS Workshop Best Presentation (Runner-Up) Award at MICCAI 2024 ASMUS Workshop Best Paper Award at ECCV 2022 Medical Computer Vision Workshop Multiple Outstanding Reviewer Awards from top conferences Dr. Jiao serves as Associate Editor for The Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), and has been actively contributing to the community as Area Chair for NeurIPS, ICML, ICLR, and ACM MM. His MIx group has secured multiple research grants including the Amazon Research Award for the PCo3D project on Physically Plausible Controllable 3D Generative Models. Current PhD students include Isaac Akintaro, Hao Ai, and Kangning Zhang. The MIx group maintains active collaborations with industry partners including Amazon and Meta, and has developed notable datasets such as 360+x for panoptic multi-modal scene understanding and DyMVHumans for dynamic human modeling. Their research bridges fundamental computer vision with practical applications in healthcare, physics, chemistry, and other scientific domains.

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Daniel Martinمشاهده پروفایل

استادیار

Saragossa UniversitySpain

Daniel Martin is an Assistant Professor at Universidad de Zaragoza, Spain, affiliated with the Graphics & Imaging Lab within the Institute of Informatics and Applications (I3A). His research focuses on computational models of visual attention and gaze behavior in virtual reality, with applications in multimodal perception, saliency prediction, and immersive content generation. Assistant Professor, Universidad de Zaragoza (2024–present) Postdoctoral Researcher, Graphics and Imaging Lab, I3A PhD in Computer Science, Universidad de Zaragoza Research Stays: Adobe Research, Meta Reality Labs Research Daniel Martin's research interests lie at the intersection of computer graphics, human perception, and machine learning. He investigates how humans perceive and interact with immersive environments, particularly focusing on visual attention, gaze patterns, and multimodal cues in virtual reality. His work spans modeling scanpaths, predicting saliency in 360° videos and 3D meshes, and understanding perceptual discomfort and change blindness in VR. His recent publications reveal a strong trend in developing data-driven and deep learning models for gaze and attention prediction, with a focus on spherical and immersive formats. He has contributed significantly to multimodal datasets and surveys, particularly in audiovisual saliency and cross-modal perception in VR. His work bridges cognitive science and computer vision, aiming to create more natural and perceptually-aware virtual experiences. SCIE-BBVA National Award for Young Computer Science Researchers (2024) CEIG 2024 Best Paper Award IEEE VR 2022 Best Journal Track Paper Award IEEE VR 2023 Best Paper Award Nominee Fulbright Predoctoral Scholarship (2022) Best PhD Thesis Award, Eurographics Spanish Chapter (EGSE) Daniel Martin has supervised and collaborated with multiple students including Edurne Bernal, Jorge Pina, Sandra Malpica, and Ana Serrano, contributing to high-impact publications and datasets. His research has been supported through prestigious grants such as the Fulbright Predoctoral Scholarship and institutional funding from Universidad de Zaragoza. He has conducted research internships at Adobe Research and Meta Reality Labs Research, indicating strong industry-academia collaboration. Daniel Martin is a key member of the Graphics & Imaging Lab at I3A, where he contributes to a vibrant research team working on cutting-edge problems in computer graphics, virtual reality, and perceptual modeling. The lab fosters interdisciplinary work combining computer vision, cognitive science, and human-centered design.

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