PhD in Compressed Sensing for Egocentric Video

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Computer vision has enjoyed much enrichment since the dawn of deep neural networks (DNNs). These improvements have largely arrived in the form of convo- lutional neural networks [2] (CNNs) or more recently, transformers [11]. Regardless of the form of the model, these techniques dominate fields where the aim is to auto- mate image and video understanding. One of the key challenges in computer vision is ensuring that the model is able to be implemented with convenient hardware (light and unobtrusive) that solves the problem in real-time. These physical constraints are compounded in the case of wearable technology, where current hardware is often unwieldy, large or expensive.