BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning

Few-shot learning is a critical task in computer vision processing that helps reduce deep learning’s reliance on large datasets. This paper aims to establish a few-shot learning network that is adaptive to diverse scenes. A novel approach referred to as task-adapted network with bi-directional long short-term memory network (BiLSTM-TANet) is proposed in this paper. BiLSTM-TANet is an end-to-end approach based on deep metric learning and designed to use the information from finite samples as much as possible. It fuses the context embeddings and structure information of the images and adaptively adjusts the features several times during the feature extraction of task to achieve task-specific embedding and quickly adapt to different distributed tasks, improves the feature extraction performance, and strikes a balance between model stability and generality. The model employs Euclidean distance as the classifier to reduce the number of model parameters and enhance the classification performance. Experiments conducted on miniImageNet, TieredImageNet, CUB200_2011 and CIFAR-FS datasets demonstrate the performance of the proposed BiLSTM-TANet. Furthermore, the effects of different few-shot learning parameters on the model’s performance are explored, providing a helpful reference for the future study of few-shot learning. Finally, a series of ablation studies are performed to analyze the performance of BiLSTM-TANet.