Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

How to efficiently transform large language models (LLMs) into instruction followers is
recently a popular research direction, while training LLM for multi-modal reasoning remains …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Large-scale contrastive vision-language pretraining has shown significant progress in visual
representation learning. Unlike traditional visual systems trained by a fixed set of discrete …

Since developing a new multimodal LLM (MLLM) by pre-training on tremendous image-text
pairs from scratch can be exceedingly resource-consuming, connecting an existing LLM with …

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by
updating only a few parameters so as to improve storage efficiency, called parameter …

Diffusion models have proven to be highly effective in generating high-quality images.
However, adapting large pre-trained diffusion models to new domains remains an open …