In this paper, we highlight a problem of evaluation metrics adopted in the
open-vocabulary segmentation. That is, the evaluation process still heavily
relies on closed-set metrics on zero-shot or cross-dataset pipelines without
considering the similarity between predicted and ground truth categories. To
tackle this issue, we first survey eleven similarity measurements between two
categorical words using WordNet linguistics statistics, text embedding, and
language models by comprehensive quantitative analysis and user study. Built
upon those explored measurements, we designed novel evaluation metrics, namely
Open mIoU, Open AP, and Open PQ, tailored for three open-vocabulary
segmentation tasks. We benchmarked the proposed evaluation metrics on 12
open-vocabulary methods of three segmentation tasks. Even though the relative
subjectivity of similarity distance, we demonstrate that our metrics can still
well evaluate the open ability of the existing open-vocabulary segmentation
methods. We hope that our work can bring with the community new thinking about
how to evaluate the open ability of models. The evaluation code is released in
github.

评估在开放词汇分割中采用的评估指标存在问题，即评估过程仍然严重依赖于零样本或交叉数据集流水线上的闭集指标，而未考虑预测和实际类别之间的相似性。为了解决这个问题，我们首先通过综合定量分析和用户研究使用 WordNet 语言统计、文本嵌入和语言模型调查了 11 种不同的相似性测量方法。在此基础上，我们设计了针对三个开放词汇分割任务的新型评估指标，即 Open mIoU、Open AP 和 Open PQ。我们在三个分割任务的 12 种开放词汇方法上对所提出的评估指标进行了基准测试。尽管相似性距离的相对主观性，我们证明了我们的指标仍然能够很好地评估现有的开放词汇分割方法的开放能力。我们希望我们的工作能够为社区带来关于如何评估模型的开放能力的新思考。评估代码在 github 上发布。

重新思考开放词汇分割的评估指标

Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion

Organizations are collecting increasingly large amounts of data for data
driven decision making. These data are often dumped into a centralized
repository, e.g., a data lake, consisting of thousands of structured and
unstructured datasets. Perversely, such mixture of datasets makes the problem
of discovering elements (e.g., tables or documents) that are relevant to a
user's query or an analytical task very challenging. Despite the recent efforts
in data discovery, the problem remains widely open especially in the two fronts
of (1) discovering relationships and relatedness across structured and
unstructured datasets where existing techniques suffer from either scalability,
being customized for a specific problem type (e.g., entity matching or data
integration), or demolishing the structural properties on its way, and (2)
developing a holistic system for integrating various similarity measurements
and sketches in an effective way to boost the discovery accuracy. In this
paper, we propose a new data discovery system, named CMDL, for addressing these
two limitations. CMDL supports the data discovery process over both structured
and unstructured data while retaining the structural properties of tables.

介绍了 CMDL 系统，它能够支持结构化和非结构化数据的发现，并在保留表格数据的结构属性的同时，有效地整合各种相似度测量和草图。

跨模态结构化和非结构化数据湖的数据发现

Cross Modal Data Discovery over Structured and Unstructured Data Lakes

Image search engines enable the retrieval of images relevant to a query
image. In this work, we consider the setting where a query for similar images
is derived from a collection of images. For visual search, the similarity
measurements may be made along multiple axes, or views, such as style and
color. We assume access to a set of feature extractors, each of which computes
representations for a specific view. Our objective is to design a retrieval
algorithm that effectively combines similarities computed over representations
from multiple views. To this end, we propose a self-supervised learning method
for extracting disentangled view-specific representations for images such that
the inter-view overlap is minimized. We show how this allows us to compute the
intent of a collection as a distribution over views. We show how effective
retrieval can be performed by prioritizing candidate expansion images that
match the intent of a query collection. Finally, we present a new querying
mechanism for image search enabled by composing multiple collections and
perform retrieval under this setting using the techniques presented in this
paper.

本文介绍了一种自我监督学习的方法，用于提取图像的特征向量，并将其组合用于图像搜索引擎的检索算法中。这种方法能有效地减少特征重叠，提高查询的精确性。

面向视觉集合扩展的自监督多视角解缴技术

Self-supervised Multi-view Disentanglement for Expansion of Visual Collections

To extract essential information from complex data, computer scientists have
been developing machine learning models that learn low-dimensional
representation mode. From such advances in machine learning research, not only
computer scientists but also social scientists have benefited and advanced
their research because human behavior or social phenomena lies in complex data.
To document this emerging trend, we survey the recent studies that apply word
embedding techniques to human behavior mining, building a taxonomy to
illustrate the methods and procedures used in the surveyed papers and highlight
the recent emerging trends applying word embedding models to non-textual human
behavior data. This survey conducts a simple experiment to warn that common
similarity measurements used in the literature could yield different results
even if they return consistent results at an aggregate level.

研究机器学习模型，学习低维表示模式，将词嵌入技术应用于人类行为挖掘，探讨其优势和趋势，并警示相似性度量常常在单独层面返回一致结果，但在聚合层面则有可能产生不同结果。