  Few-shot classification (FSC) is one of the most concerned hot issues in
recent years. The general setting consists of two phases: (1) Pre-train a
feature extraction model (FEM) with base data (has large amounts of labeled
samples). (2) Use the FEM to extract the features of novel data (with few
labeled samples and totally different categories from base data), then classify
them with the to-be-designed classifier. The adaptability of pre-trained FEM to
novel data determines the accuracy of novel features, thereby affecting the
final classification performances. To this end, how to appraise the pre-trained
FEM is the most crucial focus in the FSC community. It sounds like traditional
Class Activate Mapping (CAM) based methods can achieve this by overlaying
weighted feature maps. However, due to the particularity of FSC (e.g., there is
no backpropagation when using the pre-trained FEM to extract novel features),
we cannot activate the feature map with the novel classes. To address this
challenge, we propose a simple, flexible method, dubbed as Class-Irrelevant
Mapping (CIM). Specifically, first, we introduce dictionary learning theory and
view the channels of the feature map as the bases in a dictionary. Then we
utilize the feature map to fit the feature vector of an image to achieve the
corresponding channel weights. Finally, we overlap the weighted feature map for
visualization to appraise the ability of pre-trained FEM on novel data. For
fair use of CIM in evaluating different models, we propose a new measurement
index, called Feature Localization Accuracy (FLA). In experiments, we first
compare our CIM with CAM in regular tasks and achieve outstanding performances.
Next, we use our CIM to appraise several classical FSC frameworks without
considering the classification results and discuss them.

Computer Vision and Pattern Recognition