The concepts of overfitting and generalization are vital for evaluating
machine learning models. In this work, we show that the popular Recall@K metric
depends on the number of classes in the dataset, which limits its ability to
estimate generalization. To fix this issue, we propose a new metric, which
measures retrieval performance, and, unlike Recall@K, estimates generalization.
We apply the proposed metric to popular image retrieval methods and provide new
insights about deep metric learning generalization.

本篇论文研究了机器学习模型中过拟合和泛化的概念，发现 Recall@K 指标受到数据集中类数目的限制，提出了一种新的检索性能度量方法来估计泛化性能，应用于图像检索方法中，并对深度度量学习的泛化性能提供了新的见解。

捕捉图像检索泛化能力

Catching Image Retrieval Generalization

We present an overview of the EmotionGIF2020 Challenge, held at the 8th
International Workshop on Natural Language Processing for Social Media
(SocialNLP), in conjunction with ACL 2020. The challenge required predicting
affective reactions to online texts, and included the EmotionGIF dataset, with
tweets labeled for the reaction categories. The novel dataset included 40K
tweets with their reaction GIFs. Due to the special circumstances of year 2020,
two rounds of the competition were conducted. A total of 84 teams registered
for the task. Of these, 25 teams success-fully submitted entries to the
evaluation phase in the first round, while 13 teams participated successfully
in the second round. Of the top participants, five teams presented a technical
report and shared their code. The top score of the winning team using the
Recall@K metric was 62.47%.

该论文主要介绍了 2020 年 ACL 会议上举办的 EmotionGIF2020 比赛及其数据集（包含 40,000 条带有反应表情的推文），旨在预测用户对于在线文本的情感反应，并评估了参赛队伍的表现，获胜队伍再 Recall@K 指标下的得分为 62.47%。