@InProceedings{Zheng_2024_Large, title={Large Language Models are Good Prompt Learners for Low-Shot Image Classification}, author={Zheng, Zhaoheng and Wei, Jingmin and Hu, Xuefeng and Zhu, Haidong and Nevatia, Ram}, booktitle = {CVPR}, year = {2024}, } ...
A 2022 paper by Deng et al. [43] deals with “intra-class and inter-class” data imbalance within the task of one-shot image classification for road objects, proposing a novel GAN model named PcGAN. Compared to traditional GAN models, PcGAN places more focus on learning a robust embedding ...
These annotations are especially limited for semantic segmentation, or pixel-wise classification, of remote sensing imagery because it is labor intensive to generate image annotations. Low-shot learning algorithms can make effective inferences despite smaller amounts of annotated data. In this paper, we...
The size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few examples. We employ the recently introduced weight imprinting technique in ...
Csurka. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In ECCV, December 2012. [27] A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV, 42(3):145–175, 2001. [28] D...
MAS-MetaCL-λ30.7 ± 1.2-23.6 ± 1.4-0.3 ± 1.2 PI38.1 ± 1.0-8.3 ± 1.3-9.6 ± 1.2 PI-MetaCL-β46.1 ± 1.4-3.8 ± 0.9-3.3 ± 0.6 PI-MetaCL-λ48.7 ± 1.3-3.0 ± 0.7-2.9 ± 1.2 EWC32.7 ± 1.1-7.6 ± 2.6-17.4 ± 2.2 ...
我们的幻觉器可以整合到各种元学习者中,并提供显著的收益:当只有一个训练示例可用时,分类精度提高了6点,在具有挑战性的ImageNet low-shot 分类基准上产生了最先进的性能。 1、简介 视觉识别系统的准确性已经显著提高。但是现代识别系统仍然需要成千上万的每个类别的样本来饱和性能。这在没有足够资源来收集大型训练...
Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS)...
论文阅读笔记《Meta-learning for semi-supervised few-shot classification》 核心思想 本文提出一种基于半监督训练的小样本分类算法。所谓半监督就是在训练集中即包括带有标签的图片,也包含不带有标签的图片,作者认为人类在学习物品分类时,也会观察到许多非目标类别的物体,这种学习方式更加接近实际使用需求,并且可...
论文阅读笔记:A CLOSER LOOK AT FEW-SHOT CLASSIFICATION 类和新类之间的领域漂移现象。本文的工作:作者提出了一个详细的实验研究,以阐明小样本分类问题:1)在同一背景下对比了几种经典的小样本分类方法,证明了特征提取神经网络的重要性;2)通过将线性分类器替换...mini-ImageNet数据集和CUB数据集上的性能可以媲美几...