When human observers look at an image, attentive mechanisms drive their gaze towards salient
regions. Emulating such ability has been studied for more than 80 years by neuroscientists and by
computer vision researchers, while in the last few years, thanks to the large spread of deep
learning, saliency prediction models have achieved considerable improvement. The first part of the
talk will provide an overview of saliency prediction architectures and show the results on the most
important benchmarks for the task. The use of multi-level features and recurrent attentive
mechanisms will be discussed and their effectiveness in emulating human eye fixation mechanisms
will be demonstrated. The last part of the talk will be focused on the application of saliency
information to improve other computer vision problems, such as image captioning.
About the Speaker
Marcella Cornia received the M.Sc. degree in Computer Engineering and the Ph.D.
degree in Information and Communication Technologies from the University of Modena and Reggio
Emilia in 2016 and 2020, respectively. She is currently a postdoctoral researcher at the University
of Modena and Reggio Emilia. She works under the supervision of Prof. Rita Cucchiara on Deep
Learning and Computer Vision. She has authored or coauthored more than 20 publications in
scientific journals and international conference proceedings. Her research interests include visual
saliency prediction, image and video captioning, and multimedia technologies for cultural heritage.
She regularly serves as a Reviewer for international conferences and journals.
About the Series
The Lecture Series is organized every winter term and consist of talks with international
speakers. The speakers are experts from various fields and professions. The talks are aimed at
covering the whole spectrum of visual computing and at discussing the relevance of quantification.
This years lecture series will be held entirely online.
This is an online event only.
Registration: If you would like to attend, please email
Leonel Merino to