论文:Embedding Propagation: Smoother Manifold for Few-Shot Classification 代码:github.com/ElementAI/em Model Architecture 模型中最重要的一大创新便是Embedding Propagation。整个模型训练分为两部分,预训练和episodic fine-tuning。 Embedding Propagation (EP) 首先经过特征提取器(CNN),我们获得了一系列的特征zi∈Rm...
论文名称:Few-Shot Classification with Contrastive Learning论文地址:[2209.08224] Few-Shot Classification with Contrastive Learning (arxiv.org) 1 Intro Thanks to the available of a large amout of annotated data, deep CNN yeild impressive results on various tasks. However, the time-consuming and costly...
Cross Attention Network for Few-shot Classification ,本文的加权聚合应该将注意力吸引到目标对象上,而不是简单地突出显示支持集和查询集之间在视觉上相似的区域。基于上述分析,作者设计了一个元学习器,根据类别特征和查询特征之间的相关性自适应地生成核。元学习...,利用支持集和查询集特性之间的语义相关性来突出显...
此外,作者还针对few-shotClassification算法设定了一个新的实验场景,用来衡量跨域泛化能力。2. Related...一个综述性质的文章,但是在其基础上还提出了自己的模型,并对以前的方法进行了统一的标准。 文章的主要贡献如下: 对现有的few-shotclassification算法做了公平性的比较分析,结果显示更深 ...
In this paper, we propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of few-shot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. ...
A CLOSER LOOK AT FEW-SHOT CLASSIFICATION 一篇总结整理近来few-shot分类的文章(近来文章一些毛病:code实现细节很难说清真正的gain在哪,一些baseline被压得太低,base类和novel类之间的域差异不明显导致评估也不可能不太准)。作者复现了一下主要的几篇工作,然后总结如下:更深的backbone在不同域上的表现对于不同方法...
在少量学习分类(few-shot classification)中,我们希望能够学习这样一种算法,它仅用少数几个标记样本就可以对分类器进行训练。最近在few-shot分类中所取得的进展涵盖了元学习(meta-learning),其中,一个学习算法的参数化模型被定义,并在表示不同分类问题的事件中对其加以训练,其中每个分类问题都有一个小标记训练集和相应...
【ECCV 2022】小样本学习论文解读 | Few-Shot Class... 1.2019年后小样本是先验训练+元学习 2.多个正样本损失函数 3.无源码的论文直接pass掉
A two-stage training paradigm consisting of sequential pre-training and meta-training stages has been widely used in current few-shot learning (FSL) research. Many of these methods use self-supervised learning and contrastive learning to achieve new stat
4.3 Cross-Domain Few-Shot Classification 5 Analysis 5.1. Ablation Study 5.2 Reconstruction Visualization 6 结论 摘要 本文重点在于将few-shot分类重新标书为潜在空间中的重建问题。网络从给定类的support feature去重构query feature map,进而预测membership of the query in that class。通过支持从support feature回归...