No-Reference point cloud quality assessment (NR-PCQA) is a challenging task in computer
vision due to the irregularity of point cloud structures and the unavailability of reference …

Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have
been proposed, but they primarily focus on better fine-grained feature extraction while …

Few-Shot Segmentation (FSS) poses significant challenges due to limited support images
and large intra-class appearance discrepancies. Most existing approaches focus on aligning …

Recognizing novel sub-categories with scarce samples is an essential and challenging
research topic in computer vision. Existing literature addresses this challenge by employing …

Addressing the limitations of most few-shot learning (FSL) methods, particularly their
insufficient single-feature discriminability and generalization during pre-training encoding …

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 …

Ship target recognition is essential for maritime transportation, commercial trade, maritime
security, and monitoring illegal activity. The majority of previous ship target recognition …

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise
labels of unseen classes using only a limited number of annotated images. The key …

Few-shot remote sensing scene classification (FSRSSC) aims to identify unseen classes
only relying on very limited training samples. However, scarce training samples are …

Few-shot fine-grained fault diagnosis aims at identifying faults at a fine-grained level with
limited training samples, which is challenged by subtle category differences inherent in fine …