Generative Visual Pre-training on Unlabelled Big Data

该项目旨在基于未标记的大数据开发大规模深度神经网络的生成视觉预训练。开发准确、健壮、高效的预训练视觉模型是现代计算机视觉的基石，但它对现有的无监督表示学习提出了挑战和知识缺口。预期成果包括用于无监督视觉预训练的新理论和算法，预计将加深我们对视觉表示的理解，并使构建和部署计算机视觉应用程序和服务变得更容易。好处的例子包括用视觉智能使制造业和农业中的机器现代化。

This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training, which are anticipated to deepen our understanding of visual representation and make it easier to build and deploy computer vision applications and services. Examples of benefits include modernising machines in manufacturing and farming with visual intelligence.