  This paper presents FusionShot, a focal diversity optimized few-shot ensemble
learning approach for boosting the robustness and generalization performance of
pre-trained few-shot models. The paper makes three original contributions.
First, we explore the unique characteristics of few-shot learning to ensemble
multiple few-shot (FS) models by creating three alternative fusion channels.
Second, we introduce the concept of focal error diversity to learn the most
efficient ensemble teaming strategy, rather than assuming that an ensemble of a
larger number of base models will outperform those sub-ensembles of smaller
size. We develop a focal-diversity ensemble pruning method to effectively prune
out the candidate ensembles with low ensemble error diversity and recommend
top-$K$ FS ensembles with the highest focal error diversity. Finally, we
capture the complex non-linear patterns of ensemble few-shot predictions by
designing the learn-to-combine algorithm, which can learn the diverse weight
assignments for robust ensemble fusion over different member models. Extensive
experiments on representative few-shot benchmarks show that the top-K ensembles
recommended by FusionShot can outperform the representative SOTA few-shot
models on novel tasks (different distributions and unknown at training), and
can prevail over existing few-shot learners in both cross-domain settings and
adversarial settings. For reproducibility purposes, FusionShot trained models,
results, and code are made available at https://github.com/sftekin/fusionshot
