Continual learning requires incremental compatibility with a sequence of tasks. However,
the design of model architecture remains an open question: In general, learning all tasks …

Large-scale multi-modal contrastive learning frameworks like CLIP typically require a large
amount of image-text samples for training. However, these samples are always collected …

Supervised Continual learning involves updating a deep neural network (DNN) from an ever-
growing stream of labeled data. While most work has focused on overcoming catastrophic …

Continual learning is a sub-field of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

Continual learning requires the model to learn multiple tasks in a sequential order. To
perform continual learning, the model must possess the abilities to maintain performance on …

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may
not have been seen at training. Standard GZSL cannot handle dynamic addition of new …

The mainstream machine learning paradigms for NLP often work with two underlying
presumptions. First, the target task is predefined and static; a system merely needs to learn …

Continually learning to segment more and more types of image regions is a desired
capability for many intelligent systems. However, such continual semantic segmentation …

While traditional machine learning can effectively tackle a wide range of problems, it
primarily operates within a closed-world setting, which presents limitations when dealing …

Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …