Deep learning applications have been thriving over the last decade in many different domains, including image processing and recognition. The driver for the vibrant development of deep learning have been the availability of abundant data. This talk reviews the main results of our research activities carried out over the last few years. During this period, we have been particularly active in developing machine learning tools to process, recognize, retrieve, and classify different types of images including document, scene and satellite images. In this context, different approaches are presented like few-shot learning, active learning, multi-task learning, fine-grained attention.
Yousri Kessentini is an Associate Professor at the Digital Research Center of Sfax (CRNS) and the head of the DeepVision research team. He received his Ph.D. degree in the field of pattern recognition from the University of Rouen, France in 2009. He was a postdoctoral researcher at ITESOFT company and LITIS laboratory from 2011 to 2013. He is certified as an official instructor and ambassador from the NVIDIA Deep Learning Institute. His main research areas concern deep learning, document processing and recognition, data fusion, and computer vision. He has participated in several research projects and technology transfer projects. He has published more than 60 papers in international conferences and journals. He is also a reviewer for several international conferences and journals in the field of pattern recognition and computer vision.