Youssef Mohamed

Ph.D. Student of Computer Science and Elecrical & Computer Engineering
KAUST Visual CAIR Group

King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.
Tel: +966.548.030.350; youssef.mohamed@kaust.edu.sa

About Me

Hello there! I'm a passionate machine learning researcher, currently working on my Ph.D. at King Abdullah University of Science and Technology (KAUST). I've been lucky enough to study at some great places - I earned my Master's degree in Computer Science from the University of Tartu and my Bachelor's degree in Communication and Information Engineering from Zewail City of Science and Technology. I'm really into Multimodal Deep Learning, Affective Image Captioning, and Multicultural Modeling, and I've had the chance to share my work at some good conferences and journals. Beyond the research, I like to swim and hoop.

Research Interests

- Multicultural Modeling: Developing AI models that respect and understand cultural diversity, with an emphasis on language and culture. - Affective Computing: Emotion recognition and explanation in multimodal settings. - Multimodal Deep Learning: Integration of multiple data types, exploring how to combine different data sources effectively. Additional details: Bio, Google Scholar, ORCID: 0000-0001-5699-7362

Recent Publications (Full List):

2013-
Y. Mohamed, Runjia Li, Ibrahim Said Ahmad, K. Haydarov, Philip Torr, Kenneth Church, M. Elhoseiny:
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
EMNLP, 2024
Project page
W. Zhang, Y. Mohamed, Bernard Ghanem, Philip Torr, Adel Bibi, M. Elhoseiny:
Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation
ICLR, 2024
Y. Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li, Kenneth Church, Xiangliang Zhang, M. Elhoseiny:
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture
EMNLP, 2022
Project page
Y. Mohamed, F. Khan, K. Haydarov, M. Elhoseiny:
It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection
CVPR, 2022
Project page