Multimodal Few-Shot Learning with Frozen Language ModelsDownload PDF

May 21, 2021 (edited Jan 14, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Language Modeling, Multimodal, Few-shot Learning
  • TL;DR: We present a simple approach for transferring abilities of a frozen language model to a multi-modal setting (vision and language).
  • Abstract: 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 presented 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 any number of 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.
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