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 …

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 …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Abstract Continual Semantic Segmentation (CSS) extends static semantic segmentation by
incrementally introducing new classes for training. To alleviate the catastrophic forgetting …

Class-incremental learning (CIL) aims to train a classification model while the number of
classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity …

Learning from non-stationary data streams, also called Task-Free Continual Learning
(TFCL) remains challenging due to the absence of explicit task information in most …

Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …

Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization
capability on unseen-domain data. However, adapting pre-trained VLMs to a sequence of …

Continual learning (CL) aims to learn a non-stationary data distribution and not forget
previous knowledge. The effectiveness of existing approaches that rely on memory replay …

As there exist competitive subnetworks within a dense network in concert with Lottery Ticket
Hypothesis, we introduce a novel neuron-wise task incremental learning method, namely …