To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

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 …

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 …

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 …

In the field of class incremental learning (CIL) generative replay has become increasingly
prominent as a method to mitigate the catastrophic forgetting alongside the continuous …

In the field of Class Incremental Object Detection (CIOD) creating models that can
continuously learn like humans is a major challenge. Pseudo-labeling methods although …

In real-world settings, object detectors frequently encounter continuously emerging object
instances from new classes. Incremental Object Detection (IOD) addresses this challenge by …

The visual perception systems aim to autonomously collect consecutive visual data and
perceive the relevant information online like human beings. In comparison with the classical …

Spiking neural networks (SNNs), which mimic biological neural systems to convey
information via discrete spikes, are well-known as brain-inspired models with excellent …

While numerous methods achieving remarkable performance exist in the Object Detection
literature, addressing data distribution shifts remains challenging. Continual Learning (CL) …