few-shot classification相较于普通的分类任务,一个重大区别是测试时的小样本分类任务是训练没见过的(这很合理,如果见过那直接预测就完事了,测试阶段还要小样本作甚?)。比如在miniImageNet里,测试时的类别(category)是训练时没见过的;又比如在Meta-dataset里,不仅测试时的类别训练时没有见过,测试图片所在域(domain)...
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space[C]//European Conference on Computer Vision. Springer, Cham, 2022: 420-436. 论文地址: ecva.net/papers/eccv_20 提出了一种新颖的单阶段聚类方法: Learning Features into Clustering Space (LF2CS),该方法首先通过固定聚类...
本文是 MIT CSAIL & Google Research 在2020年关于 Few-Shot Learning的又一篇力作,受 ICLR 2020 的经典文章 A baseline for few-shot image classification 启发,提出了如下假设: Embeddings are the most critical factor to the performance of few-shot learning/meta learning algorithms; better embeddings wi...
Few-shot image classification Three regimes of image classification Problem formulation A flavor of current few-shot algorithms How well does few-shot learning work today? The key idea Transductive Learning An example Results on benchmark datasets ...
3. **Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space 该论文提出了Learning Features into Clustering Space（LF2CS）方法，通过将特征学习到聚类空间中，实现了一种基于聚类的无监督Few-shot图像分类。首先设置了一个可分离的聚类空间，使用一个可学习模型将特征映射...
IP属地: 福建 0.1592022.02.21 16:01:36字数 871阅读 790 Hello~ 两个月没更新啦 年都过了 虎年大吉呀大家 把剩下的一点关于小样本学习的论文阅读更新完~ 后续就是随缘更新啦 有需要交流可以简信啦 论文名称: 《few-shot image classification with multi-facet prototypes》 ...
Few-shot image classification is the task of doing image classification with only a few examples for each category (typically < 6 examples). Source: [Learning Embedding Adaptation for Few-Shot Learning](https://github.com/Sha-Lab/FEAT)
Few-shot Learning V.S Zero-shot Learning 小样本学习的目的是在有少量训练数据的情况下能获得准确分类测试样本的模型 零样本学习的目的是预测训练数据集中没有出现过的类 零样本学习和小样本学习有很多共同的应用,如: 图像分类 (image classification)
Recent few-shot learning methods mostly only use a single classifier to complete image classification. In general, a single classifier is likely to be overfitting because of its inherent drawbacks. However, the recognition rate of categorization will be significantly increased if we can utilize the ...
这是Masked Feature Generation Network for Few-Shot Learning(MFGN)[1]: MAE是在image-level中mask掉图像的部分patches,经过encoder-decoder,学习恢复整张图像。 MFGN是在episode-level中mask掉部分图像(保留support samples,mask掉query samples),同样经过encoder-decoder,学习恢复被mask掉的query。恢复出来的query用来...