Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote
sensing images with only a few labeled samples. The main challenges lie in small interclass …

Entity-aware image captioning aims to describe named entities and events related to the
image by utilizing the background knowledge in the associated article. This task remains …

Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of
history, it is still limited to recognizing 2 images. Recognizing objects in the physical world …

Although existing few-shot learning works yield promising results for in-domain queries, they
still suffer from weak cross-domain generalization. Limited support data requires effective …

Texture is a crucial characteristic of roads, closely related to their performance. The
recognition of pavement texture is of great significance for road maintenance professionals …

The main challenge for fine-grained few-shot image classification is to learn feature
representations with higher inter-class and lower intra-class variations, with a mere few …

Few-Shot Image Classification (FSIC) aims to identify unseen categories by a limited
number of instances. Recently, some metric-based methods have attempted to generate …

Cloud detection is one of the key technologies in the field of remote sensing. Although
extensive deep learning-based cloud detection methods achieve good performance, their …

Recently, metric-based meta-learning methods have been effectively applied to few-shot
image classification. These methods classify images based on the relationship between …

Learning the generalizable feature representation is critical to few-shot image classification.
While recent works exploited task-specific feature embedding using meta-tasks for few-shot …