TeAw: Text-Aware Few-Shot Remote Sensing Image Scene Classification

Abstract

The recent advance has shown that few-shot learning may be a promising way to alleviate the data reliance of remote sensing image scene classification. However, most existing works focus on extracting distinguishable features only from visual modality, while the problem of learning knowledge from multiple modalities has barely been visited. In this work, we propose a text-aware framework for few-shot remote sensing image scene classification (TeAw). Specifically, TeAw converts the class names to more detailed text descriptions and extracts text features using a pre-trained text encoder. Mean-while, TeAw obtains image features via an image encoder. Then we compute the correlation between the text and the image features, which helps the model grasp the core concept of the input image. Finally, TeAw calculates the similarity of local features between supports and queries to get the predictions. Extensive experiments show the outperformance of our TeAw compared with other SOTA methods.