The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

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

The availability of performant pre-trained models has led to a proliferation of fine-tuned
expert models that are specialized to a particular domain or task. Model MoErging methods …

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 …

Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …

With the emergence of Transformers and Vision-Language Models (VLMs) such as CLIP,
fine-tuning large pre-trained models has recently become a prevalent strategy in Continual …

Continual learning (CL) has garnered significant attention because of its ability to adapt to
new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a …

Multimodal Large Language Models (MLLMs) have gained significant attention due to their
impressive capabilities in multimodal understanding. However, existing methods rely heavily …

Continual learning (CL) with Vision-Language Models (VLMs) has overcome the constraints
of traditional CL, which only focuses on previously encountered classes. During the CL of …

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …