With the rapid development and prosperity of edge computing and cloud-edge collaborative platforms, the techniques of artificial intelligence (AI) are coming into our daily life. In particular, Computer Vision (CV) are a prevalent type of AI applications such as autonomous vehicles, security monitoring, and anomaly detection. When running CV applications on resource-constrained edge devices, it is critical to effectively improve the model performance and accuracy while protecting data privacy. This talk discusses the problems and challenges of such edge intelligence scenarios, studies how to improve the model training, inference, and adaption performances of CV applications, and reports latest and representative techniquenies in this area.