CAMA: Enhancing Multimodal In-Context Learning with Context-Aware Modulated Attention

Multimodal in-context learning (ICL) is emerging as a key capability that enables large vision-language models (LVLMs) to adapt to novel tasks without parameter updates, expanding their utility across various real-world applications. However, ICL remains unstable, even with well-matched in-context demonstrations (ICDs), suggesting that LVLMs struggle to fully utilize the provided context. While existing efforts focus on prompt engineering or post-hoc logit calibration, we instead investigate the underlying attention dynamics to overcome LVLMs' inherent limitations. We identify two critical deficits in their self-attention that impair effective ICL. To bridge the gap, we propose \textbf{Context-Aware Modulated Attention} (CAMA), a plug-and-play and training-free method that dynamically modulates LVLM's attention logits based on the input in-context sequence. CAMA employs a two-stage attention modulation to address both identified deficits, enhancing the focus on semantically significant tokens, particularly visual ones. Across four LVLMs and seven benchmarks, CAMA consistently outperforms vanilla models and baselines, demonstrating great effectiveness and generalization. It can also activate the desired effects of prompt engineering methods and remains robust under diverse sequence configurations. Thus, CAMA paves the way for deeper explorations of attention dynamics to advance multimodal reasoning.