MBZUAI ·
MBZUAI faculty and students presented 31 papers at the 2022 Conference on Computer Vision and Pattern Recognition (CVPR), including 6 oral presentations. Professors Fahad Khan and Shijian Lu had 9 and 8 papers accepted respectively. Researchers collaborated with 57 institutions across 16 countries. Why it matters: MBZUAI's strong showing at a top-tier CV conference demonstrates the rapid growth and international collaboration of AI research in the UAE.
MBZUAI ·
Researchers from MBZUAI and other institutions have developed a new framework called STEREO to improve the safety of text-to-image diffusion models. STEREO uses a two-stage approach: STE (Search Thoroughly Enough) based on adversarial training and REO (Robustly Erase Once) for batch concept erasure. This framework aims to enhance safety without significantly impacting the model's performance on normal queries. Why it matters: The framework addresses vulnerabilities in AI image generation, reducing the creation of inappropriate images while preserving performance on harmless queries.
MBZUAI ·
Researchers at MBZUAI, IBM Research, and other institutions have developed EarthDial, a new vision-language model (VLM) specifically designed to process geospatial data from remote sensing technologies. EarthDial handles data in multiple modalities and resolutions, processing images captured at different times to observe environmental changes. The model outperformed others on over 40 tasks including image classification, object detection, and change detection. Why it matters: This unified model bridges the gap between generic VLMs and domain-specific models, enabling complex geospatial data analysis for applications like disaster assessment and climate monitoring in the region.
MBZUAI ·
MBZUAI faculty, researchers, and students will present 34 papers at the 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Fahad Khan is a co-author on 11 accepted papers, while Salman Khan and Shijian Lu have 10 and 9 papers, respectively. One paper focuses on person image synthesis via a denoising diffusion model, and another introduces PromptCAL for generalized novel category discovery. Why it matters: This large volume of acceptances at a top-tier conference highlights MBZUAI's growing prominence and research contributions in computer vision, with potential impact across various industries from online retail to autonomous driving.
MBZUAI ·
Dr. Hao Dong from Peking University presented research on addressing the challenge of limited large-scale training data in embodied AI, particularly for manipulation, task planning, and navigation. The presentation covered simulation learning and large models. Dr. Dong is a chief scientist of China's National Key Research and Development Program and an area chair/associate editor for NeurIPS, CVPR, AAAI, and ICRA. Why it matters: Overcoming data scarcity is crucial for advancing embodied AI research and enabling more sophisticated robotic applications in the region.
MBZUAI ·
MBZUAI's Metaverse Center is developing technologies for realistic avatar generation. Hao Li and colleagues presented a novel approach at CVPR 2024, collaborating with ETH Zurich, VinAI Research, and Pinscreen. The technology addresses the challenge of mapping 2D images to 3D avatars, accounting for poses, expressions, and views. Why it matters: Creating realistic and efficient avatar generation could improve user experience and accessibility in virtual environments across the Middle East.
MBZUAI ·
MBZUAI Professor Salman Khan is researching continuous, lifelong learning systems for computer vision, aiming to mimic human learning processes like curiosity and discovery. His work focuses on learning from limited data and adversarial robustness of deep neural networks. Khan, along with MBZUAI professors Fahad Khan and Rao Anwer, and partners from other universities, presented research at CVPR 2022. Why it matters: This research has the potential to significantly improve the ability of AI systems to understand and adapt to the real world, enabling more intelligent autonomous systems.