Poster

Multimodal Few-Shot Learning with Frozen Language Models

Maria Tsimpoukelli · Jacob L Menick · Serkan Cabi · S. M. Ali Eslami · Oriol Vinyals · Felix Hill

Keywords: [ Few Shot Learning ] [ Vision ]

[ Abstract ]
Thu 9 Dec 8:30 a.m. PST — 10 a.m. PST

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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