MBZUAI has published 674 papers in 2023 and holds a global ranking of 18 in AI, CV, ML, NLP, and robotics according to CSRankings. The university presented 30 papers at ICCV, will present 53 papers at NeurIPS, and has 44 papers at EMNLP 2023. MBZUAI was also awarded its first patent by the US Patent Office for a system and method for handwriting generation. Why it matters: This demonstrates the rapid growth and increasing prominence of MBZUAI as a leading AI research institution in the region and globally.
A new paper at ICCV 2025, co-authored by MBZUAI Ph.D. student Dmitry Demidov, introduces Dense-WebVid-CoVR, a 1.6-million sample benchmark for composed video retrieval (CoVR). The benchmark features longer, context-rich descriptions and modification texts, generated using Gemini Pro and GPT-4o, with manual verification. The paper also presents a unified fusion approach that jointly reasons across video and text inputs, improving performance on fine-grained edit details. Why it matters: This work advances video search capabilities by enabling more human-like queries, which is crucial for creative and analytic workflows that require nuanced video retrieval.
MBZUAI alumnus Abdelrahman Shaker reflects on his evolving definition of impact in AI research, now valuing real-world usefulness over publication count. His work on efficient models like SwiftFormer (ICCV'23) and EdgeNext saw unexpected adoption, with millions of downloads and integration into real-world applications. Shaker chose MBZUAI for its faculty alignment, resulting in 10 publications and over 2,500 citations by graduation. Why it matters: This highlights the increasing focus on practical applications and real-world impact within the AI research community in the GCC region, as opposed to purely academic metrics.
MBZUAI researchers presented a new approach to video question answering at ICCV 2023. The method leverages insights from analyzing still images to understand video content, potentially reducing the computational resources needed for training video question answering models. Guangyi Chen, Kun Zhang, and colleagues aim to apply pre-trained image models to understand video concepts. Why it matters: This research could lead to more efficient and accessible video analysis tools, benefiting fields like healthcare and security where video data is abundant.
MBZUAI had 30 papers accepted at the International Conference on Computer Vision (ICCV) in Paris, out of 8,260 submissions. Visiting Professor Ivan Laptev served as one of the ICCV Program Chairs. Two papers from MBZUAI researchers focused on analyzing moving images, with one introducing Video-FocalNets for action analysis and the other exploring the transfer of knowledge from still image analysis to video. Why it matters: MBZUAI's strong presence at ICCV demonstrates its growing prominence in the global computer vision research landscape.
MBZUAI researchers presented a new approach to video analysis at ICCV in Paris, led by Syed Talal Wasim. The approach builds on still image processing techniques like focal modulation to analyze spatial and temporal information in video separately. It aims to improve temporal aggregation while avoiding the computational complexity of transformers. Why it matters: This research advances video understanding in computer vision by offering a more efficient method for temporal modeling, crucial for applications like activity recognition and video surveillance.