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Recent years have seen a growth in user-centric applications that requireeffective knowledge transfer across tasks in the low-data regime. An example ispersonalization, where a pretrained system is adapted by learning on smallamounts of labeled data belonging to a specific user. This setting requireshigh accuracy under low computational complexity, therefore the Pareto frontierof accuracy vs. adaptation cost plays a crucial role. In this paper we pushthis Pareto frontier in the few-shot image classification setting with a keycontribution: a new adaptive block called Contextual Squeeze-and-Excitation(CaSE) that adjusts a pretrained neural network on a new task to significantlyimprove performance with a single forward pass of the user data (context). Weuse meta-trained CaSE blocks to conditionally adapt the body of a network and afine-tuning routine to adapt a linear head, defining a method called UpperCaSE.UpperCaSE achieves a new state-of-the-art accuracy relative to meta-learners onthe 26 datasets of VTAB+MD and on a challenging real-world personalizationbenchmark (ORBIT), narrowing the gap with leading fine-tuning methods with thebenefit of orders of magnitude lower adaptation cost.

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification