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...
一、写在前面 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; bette...
这里介绍一下我们在ICML 2022中稿的一篇论文,主题和去年我们的NeurIPS论文一样,仍然是few-shot image classification/transfer,但这次的研究比之前更为深入,发现了特征表示的channel bias问题,可以算是挖到了当前视觉模型表示学习的一个核心问题。 一句话总结 本文发现无论啥预训练模型,比如ImageNet上训练的监督模型,或...
In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature...
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》 论文地址:https://arxiv.org/pdf/2102.00801.pdf ...
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)
图像分类 (image classification) 语义分割 (semantic segmentation) 图像生成 (image generation) 目标检测 (object detection) 自然语言处理 (natural language processing) 另外单样本学习 (one-shot learning) 经常会和零样本学习混在一起。单样本学习是小样本学习问题的一个特例,它的目的是从一个训练样本或图片中学...
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? 摘要 最近元学习研究的焦点一直集中在开发能够快速适应测试时间任务、数据有限且计算成本低的学习算法。小样本学习被广泛用作元学习的标准基准之一。在这项工作中,我们证明了一条简单的基线:在元训练集上学习监督或自监督表示,然后在该表...
【论文笔记】Rethinking Few-Shot Image Classification: A Good Embedding Is All You Need?,程序员大本营,技术文章内容聚合第一站。
In the issue of few-shot image classification, due to lack of sufficient data, directly training the model will lead to overfitting. In order to alleviate this problem, more and more methods focus on non-parametric data augmentation, which uses the infor