The semantic description of music metadata is a key requirement for the
creation of music datasets that can be aligned, integrated, and accessed for
information retrieval and knowledge discovery. It is nonetheless an open
challenge due to the complexity of musical concepts arising from different
genres, styles, and periods -- standing to benefit from a lingua franca to
accommodate various stakeholders (musicologists, librarians, data engineers,
etc.). To initiate this transition, we introduce the Music Meta ontology, a
rich and flexible semantic model to describe music metadata related to artists,
compositions, performances, recordings, and links. We follow eXtreme Design
methodologies and best practices for data engineering, to reflect the
perspectives and the requirements of various stakeholders into the design of
the model, while leveraging ontology design patterns and accounting for
provenance at different levels (claims, links). After presenting the main
features of Music Meta, we provide a first evaluation of the model, alignments
to other schema (Music Ontology, DOREMUS, Wikidata), and support for data
transformation.

音乐元本体论介绍了一种丰富而灵活的语义模型，用于描述与艺术家、作曲、演奏、录音和链接相关的音乐元数据，并通过数据工程的最佳实践来考虑各个利益相关者的需求和角度，从而支持数据对齐、转化和检索。

音乐元本体论：音乐元数据的互操作性的灵活语义模型

The Music Meta Ontology: a flexible semantic model for the  interoperability of music metadata

We seek to semantically describe a set of images, capturing both the
attributes of single images and the variations within the set. Our procedure is
analogous to Principle Component Analysis, in which the role of projection
vectors is replaced with generated phrases. First, a centroid phrase that has
the largest average semantic similarity to the images in the set is generated,
where both the computation of the similarity and the generation are based on
pretrained vision-language models. Then, the phrase that generates the highest
variation among the similarity scores is generated, using the same models. The
next phrase maximizes the variance subject to being orthogonal, in the latent
space, to the highest-variance phrase, and the process continues. Our
experiments show that our method is able to convincingly capture the essence of
image sets and describe the individual elements in a semantically meaningful
way within the context of the entire set. Our code is available at:
this https URL.

该研究旨在运用预先训练的视觉 - 语言模型，通过生成短语来语义上描述一组图像，从而捕捉到单个图像的属性和整个集合内部的变化，并通过对不同短语之间的相似度进行计算和比较，从而将图像集合的关键特征有效地捕捉和说明。

用文本 - PCA 描述图像集

Describing Sets of Images with Textual-PCA

Node classification is a central task in graph data analysis. Scarce or even
no labeled data of emerging classes is a big challenge for existing methods. A
natural question arises: can we classify the nodes from those classes that have
never been seen? In this paper, we study this zero-shot node classification
(ZNC) problem which has a two-stage nature: (1) acquiring high-quality class
semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well
generalized graph-based learning model. For the first stage, we give a novel
quantitative CSDs evaluation strategy based on estimating the real class
relationships, so as to get the "best" CSDs in a completely automatic way. For
the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN)
method, following the principles of locality and compositionality for zero-shot
model generalization. Finally, we conduct extensive experiments to demonstrate
the effectiveness of our solutions.

本研究探讨了零样本节点分类问题，通过提取高质量的类别语义描述并设计一种包含图学习模型的分解式原型网络方法，实现了对从未出现过的新类别节点的分类。

基于分解图原型网络的零样本节点分类

Zero-shot Node Classification with Decomposed Graph Prototype Network

Automatically describing videos has ever been fascinating. In this work, we
attempt to describe videos from a specific domain - broadcast videos of lawn
tennis matches. Given a video shot from a tennis match, we intend to generate a
textual commentary similar to what a human expert would write on a sports
website. Unlike many recent works that focus on generating short captions, we
are interested in generating semantically richer descriptions. This demands a
detailed low-level analysis of the video content, specially the actions and
interactions among subjects. We address this by limiting our domain to the game
of lawn tennis. Rich descriptions are generated by leveraging a large corpus of
human created descriptions harvested from Internet. We evaluate our method on a
newly created tennis video data set. Extensive analysis demonstrate that our
approach addresses both semantic correctness as well as readability aspects
involved in the task.

本文针对草地网球赛的广播录像，利用从互联网上获得的人类创造的描述生成丰富的语义描述，形似于体育网站上专家人类写的文本评论，经测试能提供准确性和可读性。