Events

Nov 12 - Nov 18, 2023

Efficient Algorithms for Vision and Language Learning

Jun Chen , Ph.D. Student, Computer Science

Nov 14, 12:15 - 14:15

B1 L3 R3426

The development of advanced vision-language models necessitates considerable resources, both in terms of computation and data. There is growing interest in training these models efficiently and effectively and leveraging them for various downstream tasks. This dissertation presents several contributions aimed at improving both learning and data efficiency in vision-language learning, and how to leverage them into downstream tasks.
Learning Sequential Modeling Algorithms for Motion Forecasting, Reinforcement Learning, and Vision-Language Understanding

Deyao Zhu, Ph.D. Student, Computer Science

Nov 12, 15:00 - 16:30

B1 L4 R4214

Sequential modeling algorithms have made significant strides in a variety of domains, facilitating intelligent decision-making and planning in complex scenarios. This dissertation explores the potential and limitations of these algorithms, unveiling novel approaches to enhance their performance across diverse fields, from autonomous driving and trajectory forecasting to reinforcement learning and vision language understanding.

Apr 30 - May 6, 2023

The 2nd SAAI Factory Hackathon Kickoff Symposium at KAUST

The 2nd SAAI Factory Hackathon Kickoff Symposium 2023

May 2, 09:00 - May 4, 17:00

B20 Auditorium

Hackathon artificial intelligence

We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaired by Christoph Faulhaber (Conceptual Artist, Film Maker) and Mohamed Elhoseiny (Computer Science Faculty at KAUST). About SAAI Factory Hackathon Established in 2021, SAAI Factory has been an initiative and one of the first contemporary art-based hackathons on Artificial Intelligence. The hackathon provides a framework for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to come together and explore the possibilities opened up by

Feb 12 - Feb 18, 2023

Generative Models for Neural Fields

Ivan Skorokhodov, Ph.D. Student, Computer Science

Feb 15, 20:10 - 22:00

B1 L2 R2202

Deep generative models

In computer vision, generative AI models are typically built for images, videos, and 3D objects. Recently, there has emerged a paradigm of neural fields, which unifies the representations of such types of data by parametrizing them via neural networks. In this thesis, we develop generative models for images, videos, and 3D scenes which treat the underlying data in such a form and explore the benefits which such a perspective provides.

May 15 - May 21, 2022

It is Okay to not be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection (ArtEmis 2.0)

Mohamed Elhoseiny, Associate Professor, Computer Science

May 16, 12:00 - 13:00

B9 R2322 H1

Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions.

Mar 7 - Mar 13, 2021

ArtEmis: Affective Language for Visual Art

Mohamed Elhoseiny, Associate Professor, Computer Science

Mar 8, 12:00 - 13:00

KAUST

We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice.