Abstract Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have
revolutionized visual representation learning by providing good performance on …

Ground-armored weapons have a high detection value in military operations. Satellite
synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level …

Accurate determination of similarity between samples is fundamental and critical for graph
network based few-shot learning tasks. Previous approaches typically employ convolutional …

Temporal action localization (TAL), which aims to localize actions in long untrimmed videos,
requires a large number of annotated training data. However, it is expensive to obtain …

Traditional deep learning methods require a large amount of labeled data for model training,
which is laborious and costly in real word. Few-shot learning (FSL) aims to recognize novel …

One effective approach for solving few-shot classification is learning deep representations
that measure the similarity between query images and a few support images of specific …

Few-shot fine-grained classification aims to recognize novel fine-grained categories with the
help of a few examples. Under the impact of the low inter-class and high intra-class …

针对关系网络(RN) 模型缺乏对分类任务整体相关信息的感知能力的问题,
该文提出基于任务感知关系网络(TARN) 的小样本学习(FSL) 算法. 引入模糊C 均值(FCM) …

Few-shot classification aims to classify query samples using very few labeled examples.
Most existing methods follow the Prototypical Network to classify query samples by matching …

Image classification is a fundamental problem in image processing and computer vision.
Recent algorithms have achieved significantly better results by learning deep features from …