A Baseline for Few-Shot Image ClassificationGuneet Singh DhillonPratik ChaudhariAvinash RavichandranStefano SoattoInternational Conference on Learning Representations
Title:《A New Meta-Baseline for Few-Shot Learning》Author: Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, Trevor DarrellSubmitted to ArXiv on 9 Mar 2020. 【概览】 Figure 1. Classifier-Baseline and Meta-Baseline. 如图1所示,文章提出的Baseline分为两个步骤。 首先是Classifier-Baseline。在所有...
In this paper, a pairwise-based meta learning(PML) method is proposed for few-shot image classification. Transitive transfer learning is used to fine tune the pre-trained Resnet50 model to get a feature encoder that is more suitable for few shot task. Th
Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared...
A New Meta-Baseline for Few-Shot Learning 这篇文章给出了用元学习做few-shot的baseline。整体感觉是实验做得很丰富,但是创新不太明显,简单总结一些实验过程和结论。code:https://github.com/cyvius96/few-shot-meta-baseline。 关于元学习和few-shot的基本内容有个很好的解释:Model-Agnostic Meta-Learning (...
今天介绍一篇我们组和蚂蚁网商银行在小样本视频分类领域的工作 A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark,发表于BMVC 2021。现有的小样本视频分类方法往往采用元学习范式并且十分依赖ImageNet预训练,当不使用ImageNet预训练时,这些方法的性能下降严重。通过实验,我们发现这些方法在...
(Fig.1), as well as a few narrative reviews21,22, the most suitable strategies and best practices for medical images have not been sufficiently investigated. The purpose of this work is to present a comprehensive review of deep learning models that leverage SSL for medical image classification,...
language–image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 ...
We test our approach on image classification tasks using several networks on three different datasets, namely CIFAR10, SVHN, and CINIC10.Similar content being viewed by others The Research about Recurrent Model-Agnostic Meta Learning Article 01 January 2020 Few-shot and meta-learning methods for...
源代码链接:https://github.com/yinboc/few-shot-meta-baseline 背景知识: meta-learning(元学习) 本质是一种“learning to learn”的学习过程,不同于常用的深度学习模型(依据数据集去学习如何预测或者分类),meta-learning是学习“如何更快学习一个模型”的过程 ...