Multimodal Few-Shot Learning with Frozen Language Models

作者：M. Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, O. Vinyals, Felix Hill · 发表于：Neural Information Processing Systems · 年份：2021 · 被引用次数：1011 · 研究领域：Computer Science

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks.