CoPA: Compressing Visual In-Context Learning for Parameterized Test-Time Adaptation

We study visual few-shot adaptation from labeled support examples. CoPA processes a support set with a frozen vision-language model, compresses the resulting activations with a Perceiver resampler, and uses an episodically meta-trained hypernetwork to generate low-rank updates for query inference. The backbone remains frozen and no gradients are taken at inference time. Across 14 datasets and four backbones, we evaluate predictive performance for up to 16 support examples. In this range, CoPA outperforms the frozen prompting and one-step test-time optimization baselines evaluated here. These comparisons do not isolate the effect of the architecture because CoPA also receives supervised episodic training. A separate profile up to 64 supports shows that query-phase memory and latency are approximately independent of support-set size, while support processing still grows with that size. We characterize CoPA as an amortized, support-conditioned few-shot adapter, rather than a training-free in-context learning method or an unlabeled test-time adaptation method.