Generative Visual Pre-training on Unlabelled Big Data

该项目旨在开发一种基于未标记大数据的大规模深度神经网络的生成性视觉预训练。开发准确、健壮和高效的预先训练的视觉模型是现代计算机视觉的重点，但它给现有的无监督表示学习带来了挑战和知识缺口。预期结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。好处的例子包括制造业和农业中的现代化机器和视觉智能农业。

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.