Many-Shot Visual In-Context Learning

In the video titled ‘Many-Shot VISUAL ICL is amazing! (Stanford),’ the channel code_your_own_AI explores the potential of many-shot visual in-context learning (ICL) in multimodal foundation models. The video highlights a new study by Stanford University, which demonstrates the effectiveness of using long context lengths, up to 1 million tokens, in Vision Language Models (VLMs) such as Gemini 1.5 Pro and GPT-4o. The study shows that many-shot ICL can serve as an alternative to fine-tuning, offering substantial performance gains and efficiency improvements. The models were tested with up to 1000 images in a prompt, showing that they can handle extreme context lengths effectively. The video discusses the performance of different models, emphasizing that Gemini 1.5 Pro consistently improves with more examples, while GPT-4o shows variable performance. The study also explores the impact of batching queries in a single prompt, finding that it reduces costs and latency without significant drops in performance. This research suggests that many-shot ICL can make large multimodal foundation models more adaptable and accessible for practical applications, potentially reducing the need for fine-tuning.