Video analytics with deep learning techniques has generated immense interest in academia
and industry, captivating minds with its transformative potential. Deep learning techniques …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Data drift is a thorny challenge when deploying person re-identification (ReID) models into
real-world devices, where the data distribution is significantly different from that of the …

Online video services are responsible for a large amount of global data traffic, which is why
wireless video caching has become a major focus in order to deal with the increasing …

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has
made tremendous progress in solving multiple real-world problems such as disease …

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …

Compressed edge DNN models usually experience decreasing model accuracy when
performing inference due to data drift. To maintain the inference accuracy, retraining models …

Continuous learning (CL) has emerged as one of the most popular deep learning paradigms
deployed in modern cloud GPUs. Specifically, CL has the capability to continuously update …

With the gradual maturity of statistical machine learning, it is now time to break the traditional
model of isolated learning and turn to lifelong learning, pushing machine learning to a new …

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …