Fine-grained image classificationMeta-learningData augmentationHybrid approachesComprehensive survey of methods for few-shot fine-grained image classification.Detailed analysis of benchmark datasets, including
在Cottons 数据集 中,由于每个类别只有 6 个样本,因此 1-shot 设置下每个类别选择 1 个查询样本,5-shot 设置下选择 5 个查询样本。 使用Adam 优化算法进行训练,初始学习率为 0.001,每 100,000 个 episode 后学习率乘以 0.5。 训练30 个 epoch。 2.3 测试 随机从测试集中构造 600 个 episode,每个 episode ...
2019-ICCV S3N for Fine-grained Image Recognition 装在套子里的瑶 #每日五分钟一读# Image-to-Image Translation Contrastive Learning for Unpaired Image-to-Image Translation 论文地址: https://arxiv.org/pdf/2007.15651.pdf代码地址: taesungp/contrastive-unpaired-translation关键词: 非监督… Andy 论文笔记-...
What is the main innovation introduced in the self-reconstruction network for few-shot fine-grained image classification? What challenges does fine-grained few-shot classification face regarding labeled data? How does the self-reconstruction network enhance feature diversity? How does the self-reconstruct...
cross domain 時,meta-learning 方法比 fine-tune 糟 可以把這篇當成一個 survey 吧 Optimized Generic Feature Learning for Few-shot Classification across Domains. arXiv'2001 找hyperparameter,怎感覺 approach 怪怪 而且也做 ensemble?? train/val/test: Charting the Right Manifold: Manifold Mixup for Few...
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 information of known data to construct non-pa...
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
Deep learning has proved to be very effective in learning with a large amount of labelled data. Few-shot learning in contrast attempts to learn with only a few labelled data. In this work, we develop methods for few-shot image classification from a new perspective of optimal matching between...
链接:BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification 期刊:IEEE Transactions on Image Processing 中科院分区:1区 发表时间:14 December 2020 研究动机 所提出的 Bi-Similarity Network (BSNet) 的动机。在这里,我们使用欧几里得距离和余弦距离作为特征空间中的相似性度量。欧几里得距离和...
链接:Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification 发表时间:2024-03-24 研究动机: 主干在CUB和Stanford-Dogs数据集上提取的特征的可视化结果。第一行显示了原始图像,第二行显示了从主干的最后一层提取的特征的可视化,第三行显示了从倒数第二层提取的特...