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.
KAUST's Visual Computing Center (VCC) is researching computer vision, image processing, and machine learning, with applications in self-driving cars, surveillance, and security. Professor Bernard Ghanem is working on teaching machines to understand visual data semantically, similar to how humans perceive the world. Self-driving cars use visual sensors to interpret traffic signals and detect obstacles, while computer vision also assists governments and corporations with security applications like facial recognition and detecting unattended luggage. Why it matters: Advancements in computer vision at KAUST can contribute to innovations in autonomous vehicles and enhance security measures in the region.
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.
KAUST's Visual Computing Center (VCC) hosted an Open House event on March 28, showcasing its interdisciplinary research in visual computing. Demonstrations included a virtual reality driving simulator by FalconViz, intended for driver education in Saudi Arabia. Researchers also presented a drone trained to autonomously navigate race courses and a neural network for autonomous driving using image-based technology without GPS. Why it matters: The VCC's work highlights KAUST's role in advancing visual computing applications relevant to Saudi Arabia, from driver training to autonomous systems.