Visual In-Context Learning for Large Vision-Language Models
Yucheng Zhou Xiang Li Qianning Wang Jianbing Shen
In Large Visual Language Models (LVLMs), the efficacy of In-Context Learning
(ICL) remains limited by challenges in cross-modal interactions and
representation disparities. To overcome these challenges, we introduce a novel
Visual In-Context Learning (VICL) method comprising Visual Demonstration
Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented
Demonstration Composition. Our approach retrieves images via ''Retrieval &
Rerank'' paradigm, summarises images with task intent and task-specific visual
parsing, and composes language-based demonstrations that reduce token count and
alleviate cross-modal interaction problem. Experimental evaluations on five
visual reasoning datasets demonstrate the effectiveness of our method.
Moreover, our extensive experiments leverage information flow analysis to
elucidate the effectiveness of our method, and investigate the impact of length
and position of demonstrations for LVLM. The use of in-context unlearning
further shows promise in resetting specific model knowledge without retraining.