MBZUAI researchers developed a new AI-generated image detection method called 'consistency verification' (ConV). Instead of training on labeled real and fake images, ConV identifies structural patterns unique to real photos using a data manifold concept. The system modifies images and uses DINOv2 to measure the difference between original and transformed representations, classifying images based on their proximity to the manifold. Why it matters: This approach offers a more robust way to detect AI-generated images without needing training data from every image generator, addressing a key limitation in the rapidly evolving landscape of AI image synthesis.
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.
A professor from Nanyang Technological University (NTU), Singapore gave a talk at MBZUAI about "Just-Noticeable Difference (JND)" models in visual intelligence. The talk covered visual JND models, research and applications, and future opportunities for JND modeling. JND can help tackle big data challenges with limited resources by focusing on user-centric and green systems. Why it matters: Exploring JND could lead to advancements in AI applications related to visual signal processing, image synthesis, and generative AI in the region.
Axel Sauer from the University of Tübingen presented research on scaling Generative Adversarial Networks (GANs) using pretrained representations. The work explores shaping GANs into causal structures, training them up to 40 times faster, and achieving state-of-the-art image synthesis. The presentation mentions "Counterfactual Generative Networks", "Projected GANs", "StyleGAN-XL”, and “StyleGAN-T". Why it matters: Scaling GANs and improving their training efficiency is crucial for advancing image and video synthesis, with implications for various applications in computer vision, graphics, and robotics.