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 …

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

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

Despite the great success of pre-trained language models, it is still a challenge to use these
models for continual learning, especially for the class-incremental learning (CIL) setting due …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

Class incremental learning (CIL) is a challenging setting of continual learning, which learns
a series of tasks sequentially. Each task consists of a set of unique classes. The key feature …

Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis,
which aims to extract the aspects and predict their sentiments. Most existing studies focus on …

Relying on large language models (LLMs), embodied robots could perform complex
multimodal robot manipulation tasks from visual observations with powerful generalization …

As the applications of large language models (LLMs) expand across diverse fields, the
ability of these models to adapt to ongoing changes in data, tasks, and user preferences …

Self-supervised learning has recently emerged as a strong alternative in document analysis.
These approaches are now capable of learning high-quality image representations and …