Transformers have emerged as the leading architecture in deep learning, proving to be
versatile and highly effective across diverse domains beyond language and image …

The convincing performances of deep neural networks (DNNs) can be degraded
tremendously under malicious samples, known as adversarial examples. Besides, with the …

Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad
application prospects. However, the development of this field is rigorously restricted by the …

With the emergence of DNN applications on mobile devices, plenty of attention has been
attracted to their optimization. However, the impact of DNN inference tasks on device power …

Two-stage object detectors exhibit high accuracy and precise localization, especially for
identifying small objects that are favorable for various edge applications. However, the high …

With the popularity of battery-powered edge computing, an important yet under-explored
problem is the supporting of DNNs for diverse edge devices. On the one hand, different …

Powerful machine learning algorithms have been increasingly designed to achieve better
accuracy, which however require a great amount of data and computing power relying on …