4.3 Cross-Domain Few-Shot Classification 5 Analysis 5.1. Ablation Study 5.2 Reconstruction Visualization 6 结论 摘要 本文重点在于将few-shot分类重新标书为潜在空间中的重建问题。网络从给定类的support feature去重构query feature map,进而预测me
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∈C,把全部k张图片的featur...
Few-Shot Classification with Feature Map Reconstruction Networks Davis Wertheimer*,Luming Tang*,Bharath Hariharan(* denotes equal contribution) CVPR 2021 (video) If you find our code or paper useful to your research work, please consider citing our work using the following bibtex: ...
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
Tang, B. Hariharan, Few-Shot Classification with Feature Map Reconstruction Networks, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8008–8017. Google Scholar [14] Liu Y., Bai Y., Che X., He J. Few-shot fine-grained image classification: A survey ...
A few-shot fine-grained image classification method leveraging global and local structures Article 05 March 2022 Data availibility No datasets were generated or analysed during the current study. References Wertheimer, D., Tang, L., Hariharan, B.: Few-shot classification with feature map recon...
Few-shot classification with feature map reconstruction networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. pp. 8012–8021. Woo S, Park J, Lee JY, et al. CBAM: convolutional block attention module. In: Proceedings of the European conference ...
Tang, B. Hariharan, Few-shot classification with feature map reconstruction networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8012–8021. Google Scholar [11] Y. Liu, W. Zhang, C. Xiang, T. Zheng, D. Cai, X. He, Learning to ...
链接:Few-Shot Classification with Feature Map Reconstruction Networks 发表时间:2021 研究动机 FRN的视觉直觉:我们将每个查询图像重构为支持图像分量的加权和。来自同一类的重建优于来自不同类的重建,从而实现分类。FRN 在潜在空间中执行重建,而不是图像空间,这里。
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...