Researchers from MBZUAI have developed EchoCoTr, a novel spatiotemporal deep learning method for estimating left ventricular ejection fraction (LVEF) from echocardiograms. EchoCoTr combines CNNs and vision transformers to overcome the limitations of each when applied to medical video data. The method achieves state-of-the-art results on the EchoNet-Dynamic dataset, demonstrating improved accuracy compared to existing approaches, with code available on GitHub.
MBZUAI researchers have developed an AI program using vision transformers that can learn a person's handwriting style and generate text in that style. The US Patent and Trademark Office recently granted a patent for this technology, which could aid individuals with writing impairments. The system overcomes limitations of previous GAN-based approaches by processing long-range dependencies in handwriting. Why it matters: This patented AI tool enhances personalized text generation and has potential applications in assistive technology and improving handwriting recognition models.
MBZUAI researchers developed FeSViBS, a new federated split learning technique for vision transformers that addresses data scarcity and privacy concerns in healthcare image classification. The method combines federated learning and split learning to train models collaboratively without sharing sensitive patient data directly. It overcomes limitations of traditional centralized training and vulnerabilities in federated learning. Why it matters: This approach enables the development of AI-powered healthcare applications while adhering to stringent data privacy regulations, unlocking the potential of machine learning in medical imaging.
MBZUAI researchers presented "TransRadar," a study at WACV proposing new uses for radar in object identification. The study, led by Yahia Dalbah, explores fusing radar with other technologies to identify objects, particularly for autonomous vehicles. The "TransRadar" approach uses an adaptive-directional transformer for real-time multi-view radar semantic segmentation. Why it matters: This research addresses the limitations of radar by enhancing its object recognition capabilities, potentially improving the reliability of autonomous systems in adverse conditions.
MBZUAI researchers developed GroupMamba, a new set of state-space models (SSMs) for computer vision that addresses limitations in existing SSMs related to computational efficiency and optimization challenges. GroupMamba introduces a new layer called modulated group mamba, improving efficiency and stability. In benchmark tests, GroupMamba performed as well as similar SSM systems, but more efficiently, offering a backbone for tasks like image classification, object detection, and segmentation. Why it matters: This research aims to bridge the gap between vision transformers and CNNs by improving SSMs, potentially leading to more efficient and powerful computer vision models.
MBZUAI alumnus Hanan Gani, a 2024 master's graduate in machine learning, is now a research associate at MBZUAI working on a meteorological project with the UAE government. He also focuses on multimodal and embodied intelligence research, mentors AI students, and has published nine papers during his time at MBZUAI. His research includes work on vision transformers, text-to-image generation, and large multimodal models. Why it matters: Showcases MBZUAI's role in attracting and developing AI talent within the UAE, contributing to the nation's AI research capabilities.
A new neural network architecture called Orchid was introduced that uses adaptive convolutions to achieve quasilinear computational complexity O(N logN) for sequence modeling. Orchid adapts its convolution kernel dynamically based on the input sequence. Evaluations across language modeling and image classification show that Orchid outperforms attention-based architectures like BERT and Vision Transformers, often with smaller model sizes. Why it matters: Orchid extends the feasible sequence length beyond the practical limits of dense attention layers, representing progress toward more efficient and scalable deep learning models.