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 until now. By evaluating seven different models across four architectures on three image captioning benchmarks, the research sheds light on how prompt design and architecture influence performance. This is significant as it could enhance the capabilities of VLMs, making them more efficient in understanding and generating content based on visual and textual inputs.