This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large
language models (MLLMs), where improving performance on unseen tasks often leads to a …

Deep learning has achieved tremendous success in recent years, but most of these
successes are built on an independent and identically distributed (IID) assumption. This …

Abstract Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On
top of that, exemplar-free CIL is even more challenging due to forbidden access to data of …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

Modern online platforms are increasingly employing recommendation systems to address
information overload and improve user engagement. There is an evolving paradigm in this …

We introduce a new problem in unsupervised domain adaptation, termed as Generalized
Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target …

Abstract The emergence of Vision Language Models (VLMs) is a significant advancement in
integrating computer vision with Large Language Models (LLMs) to produce detailed text …

Domain generalization (DG) is a prevalent problem in real-world applications, which aims to
train well-generalized models for unseen target domains by utilizing several source …