What do vision-language models see in the context? Investigating multimodal in-context learning

A recent study delves into the effectiveness of in-context learning (ICL) in vision-language models (VLMs), a topic that has not been thoroughly explored despite the success of ICL in large language models. By evaluating seven different models across various architectures on three image captioning benchmarks, the research sheds light on how prompt design and architecture influence performance. This work is significant as it could enhance our understanding of multimodal learning, potentially leading to advancements in AI applications that require both visual and textual comprehension.