1.整体架构 C2-Net的整体架构旨在解决Few-Shot Fine-Grained Image Classification (FS-FGIC)任务中的两个关键问题:特征提取时的噪声抑制和支持集与查询集之间的特征匹配。 2.跨层特征精炼(CLFR)模块 目标:在满足细粒度分类任务需求的同时,抑制样本级别的噪声和不可泛化的特征,减轻Few-Shot Learning设置下的过拟合...
2. fine-grained image classification -- CUB 3. cross-domain adaptation -- mini-ImageNet -> CUB 结果翻译过来就是--换成cosine similarity classifier的baseline就非常的有用,比你们的有些还有用。 然后,我们现在才用了4-layer backbone(Conv-4 backbone),改用更深的看看。 在CUB上,类别之间domain shift...
Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary d...
Order Optimal One-Shot Distributed Learning 方向:分布式学习 问题:分布式统计优化 方法:多决策估算算法 Learning to Self-Train for Semi-Supervised Few-Shot Classification 方向:图像分类 问题:半监督少样本分类任务 方法:提出一种半监督元学习方法,自训练学习 Zero-shot Learning via Simultaneous Generating and Lea...
mini-ImageNet ... 2.3 motivation Few-shot classification中一直有这样一个问题:meta-learning 和 representation-learning 哪个表现更好。最近的文章发现仅用常规的模型做 fine-tuning,就可以达到与最好的meta-learning方法差不多的效果。因此本文探索在Few-shot classification中如何学习好的 representation。
Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar objects from different sub-categories with limited supervision. However, traditional few-shot learning solutions typically exploit image-level features and are committed to capturing global silhouettes while accidentally ig...
Moreover, as defect images are scarce, defect detection becomes a few-shot sample task falling under fine-grained visual classification (FGVC). However, in practical scenarios, discerning defects for model differentiation is complicated by the need to capture subtle features and nuances among ...
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)
Fine tuning现有参数 Efficient k-shot learning with regularized deep networks, in AAAI, 2018. CLEAR: Cumulative learning for one-shot one-class image recognition, in CVPR, 2018. Learning structure and strength of CNN filters for small sample size training, in CVPR, 2018. ...
[20] Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan,and Yinghui Xu. Few-shot incremental learning with contin-ually evolved classifiers. [21] Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola. Rethinking few-shot image classification: a good embedding ...