2018年5月23日 · In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates.

2020年6月9日 · 本文在基于度量学习的小样本 算法 的基础上提出了几点改进方案：度量放缩（Metric Scaling），任务条件（Task Conditioning）以及辅助任务合作训练（Auxiliary task co-training）。 首先，对于度量函数. p ( y = k ∣ x ) = s o f t m a x ( − α d ( z , c k ) ) p (y=k|x)=softmax (-\alpha d (z,c_k)) p(y = k∣x) = sof tmax(−αd(z,ck))。

Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task.

2019年7月15日 · 第一步：利用Auxiliary task co-training的思想训练feature extractor，这样该extractor便可以作为 特征提取器 得到support set和query set中样例的初始Class Representation。 文中主要是随机从mini-imagenet上抽取64个样本组成一个batch对feature extractor进行训练。

Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space.

2024年12月20日 · TADAM 是一个由 ServiceNow 研究团队开发的开源项目，旨在通过任务依赖的自适应度量（Task-Dependent Adaptive Metric）来改进少样本学习（Few-Shot Learning）。 该项目的主要目标是解决在数据稀缺的情况下，如何有效地进行模型训练和预测的问题。

TADAM is a ServiceNow Research project that was started at Element AI. Cannot retrieve latest commit at this time. ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.

2021年10月17日 · 这是最近公开的CVPR2021主会议论文中一篇MOT方向的论文，将位置预测和特征提取两个任务协同工作，从而有效改善了遮挡等问题。 简介 目前的多目标跟踪方法主要关注于两个方向来改进跟踪性能，一是基于跟踪信息从之…

2020年2月19日 · Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a...

Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task.