Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …

Humans have a natural instinct to identify unknown object instances in their environments.
The intrinsic curiosity about these unknown instances aids in learning about them, when the …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks
when learning new ones. In this paper we focus on class incremental continual learning in …

Traditional object detectors are ill-equipped for incremental learning. However, fine-tuning
directly on a well-trained detection model with only new data will lead to catastrophic …

Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing
deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on …

Incremental object detection (IOD) aims to train an object detector in phases, each with
annotations for new object categories. As other incremental settings, IOD is subject to …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Training deep neural networks at the edge on light computational devices, embedded
systems and robotic platforms is nowadays very challenging. Continual learning techniques …