MBZUAI students won an award at the Asian Conference on Computer Vision (ACCV) for their ObjectCompose method. ObjectCompose generates object-to-background variations of images to validate neural network performance. It helps developers test AI systems by adding variability to validation datasets without distorting the main object. Why it matters: This research offers a new approach to improve the robustness and reliability of computer vision models, which is crucial for real-world applications in the region.
Holger Pirk from Imperial College London is developing a novel approach to data management system composition called BOSS. The system uses a homoiconic representation of data and code and partial evaluation of queries by components, drawing inspiration from compiler-construction research. BOSS achieves a fully composable design that effectively combines different data models, hardware platforms, and processing engines, enabling features like GPU acceleration and generative data cleaning with minimal overhead. Why it matters: This research on composable database systems can broaden the applicability of data management techniques in the GCC region, enabling more flexible and efficient data processing for various applications.
A new approach to composed video retrieval (CoVR) is presented, which leverages large multimodal models to infer causal and temporal consequences implied by an edit. The method aligns reasoned queries to candidate videos without task-specific finetuning. A new benchmark, CoVR-Reason, is introduced to evaluate reasoning in CoVR.
KAUST Professor Wolfgang Heidrich is researching computational imaging systems that jointly design optics and image reconstruction algorithms. He focuses on hardware-software co-design for imaging systems with applications in HDR, compact cameras, and hyperspectral imaging. Heidrich's work on HDR displays was the basis for Brightside Technologies, acquired by Dolby in 2007. Why it matters: This research aims to advance imaging technology through AI-driven design, potentially impacting various fields from consumer electronics to scientific research within the region and globally.
Researchers from Carnegie Mellon University and MBZUAI have developed a new method called ConceptAligner for precise image editing using AI. The system decomposes text embeddings into independent building blocks called atomic concepts, allowing users to make targeted tweaks without generating entirely new images. Their approach ensures that each latent factor maps to a specific user-controllable dial, enabling accurate concept-level modifications. Why it matters: This research addresses a major limitation in AI image generation, enhancing its usefulness in industries where precise control is crucial, such as advertising and medicine, and improving the reliability of AI-driven creative tools.
This paper introduces a new Single Domain Generalization (SDG) method called ConDiSR for medical image classification, using channel-wise contrastive disentanglement and reconstruction-based style regularization. The method is evaluated on multicenter histopathology image classification, achieving a 1% improvement in average accuracy compared to state-of-the-art SDG baselines. Code is available at https://github.com/BioMedIA-MBZUAI/ConDiSR.