New framework enhances multimodal in-context learning with taxonomy and corpus

Researchers have developed UniICL, a framework designed to improve in-context learning (ICL) for unified multimodal models. This approach addresses the sensitivity of ICL to example selection and formatting, which is particularly challenging in models that handle both understanding and generation across different modalities. UniICL introduces a six-level taxonomy to categorize demonstration roles and a large-scale corpus, UniICL-760K, to facilitate ICL episodes. Additionally, a plug-and-play module called the Context-Adaptive Prototype Modulator is proposed to enhance few-shot stability. Evaluations on the UniICL-Bench demonstrate that this method achieves competitive results, often surpassing larger multimodal models on understanding-focused ICL tasks. AI

IMPACT This research could lead to more stable and effective few-shot learning in multimodal AI systems, improving their adaptability to new tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework and dataset for multimodal in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Context-Adaptive Prototype Modulator
• in-context learning
• multimodal models
• UniICL
• UniICL-760K
• UniICL-Bench
• Yicheng Xu

• paper
• model release

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