  Large Language Models like ChatGPT demonstrate a remarkable capacity to learn
new concepts during inference without any fine-tuning. However, visual models
trained to detect new objects during inference have been unable to replicate
this ability, and instead either perform poorly or require meta-training and/or
fine-tuning on similar objects. In this work, we propose a meta-learning
algorithm that emulates Large Language Models by learning new visual concepts
during inference without fine-tuning. Our approach leverages a frozen
pre-trained feature extractor, and analogous to in-context learning, recasts
visual meta-learning as sequence modeling over datapoints with known labels and
a test datapoint with an unknown label. On 8 out of 11 meta-learning
benchmarks, our approach -- without meta-training or fine-tuning -- exceeds or
matches the state-of-the-art algorithm, P>M>F, which is meta-trained on these
benchmarks. Our code is available at https://github.com/cfifty/CAML.
