Estimating causal effects from randomized experiments is only feasible if
participants agree to reveal their potentially sensitive responses. Of the many
ways of ensuring privacy, label differential privacy is a widely used measure
of an algorithm's privacy guarantee, which might encourage participants to
share responses without running the risk of de-anonymization. Many
differentially private mechanisms inject noise into the original data-set to
achieve this privacy guarantee, which increases the variance of most
statistical estimators and makes the precise measurement of causal effects
difficult: there exists a fundamental privacy-variance trade-off to performing
causal analyses from differentially private data. With the aim of achieving
lower variance for stronger privacy guarantees, we suggest a new differential
privacy mechanism, "Cluster-DP", which leverages any given cluster structure of
the data while still allowing for the estimation of causal effects. We show
that, depending on an intuitive measure of cluster quality, we can improve the
variance loss while maintaining our privacy guarantees. We compare its
performance, theoretically and empirically, to that of its unclustered version
and a more extreme uniform-prior version which does not use any of the original
response distribution, both of which are special cases of the "Cluster-DP"
algorithm.

基于随机实验估计因果效应只有在参与者同意透露潜在敏感回应的情况下才可行。我们提出了一种新的差分隐私机制 “Cluster-DP”，通过利用数据的任何给定的聚类结构来实现更强的隐私保证和更低的方差损失，同时仍然允许因果效应的估计。

具有差分隐私的因果推断（聚类）结果

Causal Inference with Differentially Private (Clustered) Outcomes

Cross-modal retrieval between videos and texts has gained increasing research
interest due to the rapid emergence of videos on the web. Generally, a video
contains rich instance and event information and the query text only describes
a part of the information. Thus, a video can correspond to multiple different
text descriptions and queries. We call this phenomenon the ``Video-Text
Correspondence Ambiguity'' problem. Current techniques mostly concentrate on
mining local or multi-level alignment between contents of a video and text
(\textit{e.g.}, object to entity and action to verb). It is difficult for these
methods to alleviate the video-text correspondence ambiguity by describing a
video using only one single feature, which is required to be matched with
multiple different text features at the same time. To address this problem, we
propose a Text-Adaptive Multiple Visual Prototype Matching model, which
automatically captures multiple prototypes to describe a video by adaptive
aggregation of video token features. Given a query text, the similarity is
determined by the most similar prototype to find correspondence in the video,
which is termed text-adaptive matching. To learn diverse prototypes for
representing the rich information in videos, we propose a variance loss to
encourage different prototypes to attend to different contents of the video.
Our method outperforms state-of-the-art methods on four public video retrieval
datasets.

本文提出了一种文本自适应多视觉原型匹配模型，通过自适应聚合视频标记特征来描述视频，以解决视频和文本之间的关联模糊问题，而且此方法表现优于当前公共视频检索数据集上的最新技术。

文本自适应的多视觉原型匹配用于视频检索

Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval

In this paper, we address the problem of unsupervised video summarization
that automatically extracts key-shots from an input video. Specifically, we
tackle two critical issues based on our empirical observations: (i) Ineffective
feature learning due to flat distributions of output importance scores for each
frame, and (ii) training difficulty when dealing with long-length video inputs.
To alleviate the first problem, we propose a simple yet effective
regularization loss term called variance loss. The proposed variance loss
allows a network to predict output scores for each frame with high discrepancy
which enables effective feature learning and significantly improves model
performance. For the second problem, we design a novel two-stream network named
Chunk and Stride Network (CSNet) that utilizes local (chunk) and global
(stride) temporal view on the video features. Our CSNet gives better
summarization results for long-length videos compared to the existing methods.
In addition, we introduce an attention mechanism to handle the dynamic
information in videos. We demonstrate the effectiveness of the proposed methods
by conducting extensive ablation studies and show that our final model achieves
new state-of-the-art results on two benchmark datasets.

本文提出了一种解决自动从输入视频中提取关键镜头的无监督视频摘要问题的方法，并针对我们的经验观察处理了两个关键问题：由于每帧的输出重要性得分是平坦分布的而导致的无效特征学习和处理长视频输入时的训练困难问题。为了缓解第一个问题，我们提出了一个称为方差损失的简单而有效的正则化损失项。对于第二个问题，我们设计了一种新颖的两流网络，名为 “块和步距网络（CSNet）”，它利用了视频特征上的本地（块）和全局（步距）时间视图，针对长视频的摘要结果比现有方法更好。此外，我们还引入了一种注意机制来处理视频中的动态信息。通过进行全面的消融研究，我们证明了所提出方法的有效性，并展示了我们的最终模型在两个基准数据集上实现了新的最优结果。