Few-shot Image Classification based on Attribute Prediction and Selection

Few-shot learning addresses the challenges of image classification with limited samples, but current methods often fail to fully utilize sample correlations and external semantic information, leading to low accuracy. To overcome these limitations, we propose a few-shot image classification method based on attribute prediction and selection. In addition to the image branch that extracts image features, an extra attribute branch employs an attribute prediction network to predict attribute features of the query set samples. This ensures that both the support set and the query set can utilize attribute information as assistance. Additionally, we propose an attribute selection network to distinguish and select discriminative attribute features, thereby increasing the utilization of task-related attribute information. Finally, the processed attribute features and the image features are merged together as the ultimate features for final classification. Extensive experiments on mainstream benchmarks demonstrate the superiority of our method.