FRN 通过特征图重构(Feature Map Reconstruction)来实现少样本分类,利用支持集(support set)的特征图重构查询样本(query sample)的特征图,并根据重构误差判断类别归属。 1.问题定义 对于一个N-way, K-shot的少样本分类任务: 支持集(Support Set): 包含N个类别,每个类别有K张已标注的样本。 查询样本(Query Sample...
4.2 General Few-Shot Classification 4.3 Cross-Domain Few-Shot Classification 5 Analysis 5.1. Ablation Study Training shot:发现在1-shot episode中训练的FRN模型识别表现不如5-shot episode Pre-training:FRN预训练对于竞技性的few-shot表现至关重要,尤其是与预训练极限相比,然而,仅仅进行预训练并不能培养出一个...
Few-Shot Classification with Feature Map Reconstruction Networks Davis Wertheimer* Luming Tang* Bharath Hariharan Cornell University {dww78,lt453,bh497}@cornell.edu Abstract In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The...
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propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of fewshot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. feature map and feature...
论文地址:https://arxiv.org/pdf/1711.06025.pdf简介:关系网络的做法是将query set的特征表示和support set的特征表示concat一起,然后走mlp网络,最后softmax得到分类结果。当few-shot的情况,将同一类的feature_map进行相加。网络结构: Few-Shot Text Classification with Induction Network 2018 ...
In this paper, we propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of few-shot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. ...
Feature Map Ridge Regression 一些符号的解释: Support set: Xs n-way, k-shot 目标是预测输入的query image xq的标签yq xq经过特征提取器,提取出来的feature map为Q∈Rr×d。其中r是feature map的spatial resolution(高乘宽),d是channel数。 对于每个class c\in C,把全部k张图片的fe...
Few-Shot Classification With Feature Map Reconstruction Networks Davis Wertheimer, Luming Tang, Bharath Hariharan few-short问题中因为训练集不多,导致常见分类方法效果不理想,本文提出在特征空间中基于重构误差做图像分类 训练集每张图映射到特征空间,相同类别的组织在一起,$S_c$表示c类中用k个样本的特征。待分...