Members of IVAL at MBZUAI conduct research primarily in Multi-modal learning, Remote sensing and earth vision, Person centric vision, Synthesis and Generation, Visual Recognition with Limited Supervision and Medical Image Analysis
Deep learning with multiple modalities of data including images, videos, text and audio. Our team at IVAL explores how multi-modal understanding can be used for improving generalization in vision applications such as open-vocabulary object detection, and visual question-answering.
Remote Sensing and Earth Vision
Remote sensing data provide an unbiased, uninterrupted and borderless view of human activities and natural processes. Powered by data collected from diverse satellite systems, deep-learning models can be used to gain insight to guide targeted actions in remote locations.
Medical Image Analysis
Our team at IVAL investigates real-world healthcare problems using medical imaging with deep learning and computer vision. We are also interested in investigating fundamental concepts in medical imaging such as multi-organ segmentation, medical image generation and deep-learning architectures for medical imaging.