Recently, video summarization has been proposed as a method to help video
exploration. However, traditional video summarization models only generate a
fixed video summary which is usually independent of user-specific needs and
hence limits the effectiveness of video exploration. Multi-modal video
summarization is one of the approaches utilized to address this issue.
Multi-modal video summarization has a video input and a text-based query input.
Hence, effective modeling of the interaction between a video input and
text-based query is essential to multi-modal video summarization. In this work,
a new causality-based method named Causal Video Summarizer (CVS) is proposed to
effectively capture the interactive information between the video and query to
tackle the task of multi-modal video summarization. The proposed method
consists of a probabilistic encoder and a probabilistic decoder. Based on the
evaluation of the existing multi-modal video summarization dataset,
experimental results show that the proposed approach is effective with the
increase of +5.4% in accuracy and +4.92% increase of F 1- score, compared with
the state-of-the-art method.

本文提出了基于因果关系的视频摘要器（CVS），它采用了概率编码器和概率解码器来有效地捕捉视频和查询之间的交互信息，从而提高了多模态视频摘要的精度。与现有的方法相比，CVS 的准确度提高了 5.4％，F 1 - 得分提高了 4.92％。

视频探索的因果关系视频摘要器

Causal Video Summarizer for Video Exploration

Despite the recent progress in hyperparameter optimization (HPO), available
benchmarks that resemble real-world scenarios consist of a few and very large
problem instances that are expensive to solve. This blocks researchers and
practitioners not only from systematically running large-scale comparisons that
are needed to draw statistically significant results but also from reproducing
experiments that were conducted before. This work proposes a method to
alleviate these issues by means of a meta-surrogate model for HPO tasks trained
on off-line generated data. The model combines a probabilistic encoder with a
multi-task model such that it can generate inexpensive and realistic tasks of
the class of problems of interest. We demonstrate that benchmarking HPO methods
on samples of the generative model allows us to draw more coherent and
statistically significant conclusions that can be reached orders of magnitude
faster than using the original tasks. We provide evidence of our findings for
various HPO methods on a wide class of problems.

提出了一种通过元代理模型来解决超参优化问题中的样本不足和较大问题实例计算昂贵的方法，该模型结合了概率编码器和多任务模型，可生成成本低廉且逼真的诸如感兴趣问题类别的任务，表明在样本生成模型上进行超参数优化方法的基准测试，可以比使用原始任务快几个数量级地得出更一致和具有统计显著性的结论。

超参数优化的元代理基准测试

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Learning compact discrete representations of data is a key task on its own or
for facilitating subsequent processing of data. In this paper we present a
model that produces Discrete InfoMax Codes (DIMCO); we learn a probabilistic
encoder that yields k-way d-dimensional codes associated with input data. Our
model's learning objective is to maximize the mutual information between codes
and labels with a regularization, which enforces entries of a codeword to be as
independent as possible. We show that the infomax principle also justifies
previous loss functions (e.g., cross-entropy) as its special cases. Our
analysis also shows that using shorter codes, as DIMCO does, reduces
overfitting in the context of few-shot classification. Through experiments in
various domains, we observe this implicit meta-regularization effect of DIMCO.
Furthermore, we show that the codes learned by DIMCO are efficient in terms of
both memory and retrieval time compared to previous methods.

本文提出一种能够生成离散信息最大化码（DIMCO）的模型，该模型使用了概率编码器，可以产生与输入数据相关联的 k 路 d 维码。我们的学习目标是最大化代码和标签之间的互信息，同时使用正则化来强制码字的项尽可能独立。我们的分析表明，使用 DIMCO 这样的短代码可以在少样本分类的情况下减少过拟合，并且相对于以前的方法，DIMCO 获取的代码在内存和检索时间方面也更加高效。