3 papers
cs.CL2026
Towards Localized and Disentangled Knowledge Editing for Multimodal Large Language Models
Leijiang Gu, Zhen Zeng, Feng Li +2
Existing methods in Multimodal Knowledge Editing (MKE) have advanced the ability to correct outdated or inaccurate knowledge in Multimodal Large Language Models (MLLMs). However, t…
cs.LG2024
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
Fan Wang, Zhongyi Han, Xingbo Liu +2
In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA),…
cs.CV2024
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions. Despite this, domai…