In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal
large language models (MLLMs) have garnered significant attention due to their powerful …

Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language
Models (LVLMs) to meet individual task requirements. To date, most of the existing …

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

In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new
continual learning scenario for Multimodal Large Language Models (MLLMs) that involves …

Continual learning (CL) aims to empower machine learning models to learn continually from
new data, while building upon previously acquired knowledge without forgetting. As …

Vision-Language Models (VLMs) have shown significant promise in Visual Question
Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these …

Instruction tuning guides the Multimodal Large Language Models (MLLMs) in aligning
different modalities by designing text instructions, which seems to be an essential technique …

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 …

Current deep learning models for time series often face challenges with generalizability in
scenarios characterized by limited samples or inadequately labeled data. By tapping into the …