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 …

Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting
caused by the classifier's bias towards the current task has long posed a significant …

Continual learning (CL) is a learning paradigm that emulates the human capability of
learning and accumulating knowledge continually without forgetting the previously learned …

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 …

Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing and computer vision. Unlike traditional neural …

Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding
model's capacity to segment new class of images supervised by only a few samples …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Despite the significant recent progress made on 3D point cloud semantic segmentation, the
current methods require training data for all classes at once, and are not suitable for real-life …