  Low-shot image classification is a fundamental task in computer vision, and
the emergence of large-scale vision-language models such as CLIP has greatly
advanced the forefront of research in this field. However, most existing
CLIP-based methods lack the flexibility to effectively incorporate other
pre-trained models that encompass knowledge distinct from CLIP. To bridge the
gap, this work proposes a simple and effective probabilistic model ensemble
framework based on Gaussian processes, which have previously demonstrated
remarkable efficacy in processing small data. We achieve the integration of
prior knowledge by specifying the mean function with CLIP and the kernel
function with an ensemble of deep kernels built upon various pre-trained
models. By regressing the classification label directly, our framework enables
analytical inference, straightforward uncertainty quantification, and
principled hyper-parameter tuning. Through extensive experiments on standard
benchmarks, we demonstrate that our method consistently outperforms competitive
ensemble baselines regarding predictive performance. Additionally, we assess
the robustness of our method and the quality of the yielded uncertainty
estimates on out-of-distribution datasets. We also illustrate that our method,
despite relying on label regression, still enjoys superior model calibration
compared to most deterministic baselines.
