2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs
Ziqi Wang, Chang Che, Qi Wang +4
While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominantly focus on models without safe…
StrLoRA: Towards Streaming Continual Visual Instruction Tuning for MLLMs
Chang Che, Ziqi Wang, Hui Ma +2
Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models to incrementally acquire new abilities. However, existing CVIT methods operate under a restricti…
LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning
Chang Che, Ziqi Wang, Pengwan Yang +3
Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models (MLLMs) to incrementally learn new tasks over time. However, this process is challenged by catas…
SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning
Ziqi Wang, Chang Che, Qi Wang +3
Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions.…