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 …

In incremental learning, replaying stored samples from previous tasks together with current
task samples is one of the most efficient approaches to address catastrophic forgetting …

With the emergence of foundation models, deep learning-based object detectors have
shown practical usability in closed set scenarios. However, for real-world tasks, object …

Abstract The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually
learn new knowledge while preventing forgetting of the old knowledge, without storing any …

In incremental object detection, knowledge distillation has been proven to be an effective
way to alleviate catastrophic forgetting. However, previous works focused on preserving the …

Background We developed machine learning models that combine preoperative and
intraoperative risk factors to predict mortality after cardiac surgery. Methods Machine …

The growing amount of data demands methods that can gradually learn from new samples.
However, it is not trivial to continually train a network. Retraining a network with new data …

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

This paper investigates the problem of class-incremental object detection for agricultural
applications where a model needs to learn new plant species and diseases incrementally …

Object detection with the capacity to incrementally adapt to new domains is a crucial yet
relatively under-explored research topic. The catastrophic forgetting problem presents a …