  In recent years, researchers pay growing attention to the few-shot learning
(FSL) task to address the data-scarce problem. A standard FSL framework is
composed of two components: i) Pre-train. Employ the base data to generate a
CNN-based feature extraction model (FEM). ii) Meta-test. Apply the trained FEM
to the novel data (category is different from base data) to acquire the feature
embeddings and recognize them. Although researchers have made remarkable
breakthroughs in FSL, there still exists a fundamental problem. Since the
trained FEM with base data usually cannot adapt to the novel class flawlessly,
the novel data's feature may lead to the distribution shift problem. To address
this challenge, we hypothesize that even if most of the decisions based on
different FEMs are viewed as weak decisions, which are not available for all
classes, they still perform decently in some specific categories. Inspired by
this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM),
which comprehensively considers the decisions based on multiple FEMs to enhance
the efficacy and robustness of the model. MDFM is a simple, flexible,
non-parametric method that can directly apply to the existing FEMs. Besides, we
extend the proposed MDFM to two FSL settings (i.e., supervised and
semi-supervised settings). We evaluate the proposed method on five benchmark
datasets and achieve significant improvements of 3.4%-7.3% compared with
state-of-the-arts.
