Email continues to be a pivotal and extensively utilized communication medium
within professional and commercial domains. Nonetheless, the prevalence of spam
emails poses a significant challenge for users, disrupting their daily routines
and diminishing productivity. Consequently, accurately identifying and
filtering spam based on content has become crucial for cybersecurity. Recent
advancements in natural language processing, particularly with large language
models like ChatGPT, have shown remarkable performance in tasks such as
question answering and text generation. However, its potential in spam
identification remains underexplored. To fill in the gap, this study attempts
to evaluate ChatGPT's capabilities for spam identification in both English and
Chinese email datasets. We employ ChatGPT for spam email detection using
in-context learning, which requires a prompt instruction and a few
demonstrations. We also investigate how the training example size affects the
performance of ChatGPT. For comparison, we also implement five popular
benchmark methods, including naive Bayes, support vector machines (SVM),
logistic regression (LR), feedforward dense neural networks (DNN), and BERT
classifiers. Though extensive experiments, the performance of ChatGPT is
significantly worse than deep supervised learning methods in the large English
dataset, while it presents superior performance on the low-resourced Chinese
dataset, even outperforming BERT in this case.

本研究旨在评估 ChatGPT 在英文和中文电子邮件数据集上用于垃圾邮件识别的能力，并探讨训练样本大小对 ChatGPT 性能的影响。通过在上下文中学习，我们使用 ChatGPT 进行垃圾邮件检测，并将其性能与朴素贝叶斯、支持向量机 (SVM)、逻辑回归 (LR)、前馈神经网络 (DNN) 和 BERT 分类器等五种流行的基准方法进行比较。尽管在大型英文数据集中，ChatGPT 的性能明显差于深度监督学习方法，但在低资源的中文数据集上表现优异，甚至超越了 BERT。

评估 ChatGPT 的垃圾邮件检测性能

Evaluating the Performance of ChatGPT for Spam Email Detection

We introduce the task of localizing a flexible number of objects in
real-world 3D scenes using natural language descriptions. Existing 3D visual
grounding tasks focus on localizing a unique object given a text description.
However, such a strict setting is unnatural as localizing potentially multiple
objects is a common need in real-world scenarios and robotic tasks (e.g.,
visual navigation and object rearrangement). To address this setting we propose
Multi3DRefer, generalizing the ScanRefer dataset and task. Our dataset contains
61926 descriptions of 11609 objects, where zero, single or multiple target
objects are referenced by each description. We also introduce a new evaluation
metric and benchmark methods from prior work to enable further investigation of
multi-modal 3D scene understanding. Furthermore, we develop a better baseline
leveraging 2D features from CLIP by rendering object proposals online with
contrastive learning, which outperforms the state of the art on the ScanRefer
benchmark.

我们介绍了使用自然语言描述来定位现实世界 3D 场景中多个对象的任务。我们提出了 Multi3DRefer，扩展了 ScanRefer 数据集和任务，并引入了新的评估指标和基准方法以进一步研究多模态 3D 场景理解。此外，我们利用 CLIP 的 2D 特征和对比学习在线渲染对象提案，构建了更好的基准线，该基准线在 ScanRefer 基准测试上超越了最新技术。

Multi3DRefer: 文本描述与多个 3D 对象的关联

Multi3DRefer: Grounding Text Description to Multiple 3D Objects

Spatio-temporal action detection is an important and challenging problem in
video understanding. The existing action detection benchmarks are limited in
aspects of small numbers of instances in a trimmed video or low-level atomic
actions. This paper aims to present a new multi-person dataset of
spatio-temporal localized sports actions, coined as MultiSports. We first
analyze the important ingredients of constructing a realistic and challenging
dataset for spatio-temporal action detection by proposing three criteria: (1)
multi-person scenes and motion dependent identification, (2) with well-defined
boundaries, (3) relatively fine-grained classes of high complexity. Based on
these guide-lines, we build the dataset of MultiSports v1.0 by selecting 4
sports classes, collecting 3200 video clips, and annotating 37701 action
instances with 902k bounding boxes. Our datasets are characterized with
important properties of high diversity, dense annotation, and high quality. Our
Multi-Sports, with its realistic setting and detailed annotations, exposes the
intrinsic challenges of spatio-temporal action detection. To benchmark this, we
adapt several baseline methods to our dataset and give an in-depth analysis on
the action detection results in our dataset. We hope our MultiSports can serve
as a standard benchmark for spatio-temporal action detection in the future. Our
dataset website is at this https URL

本文介绍了一个新的多人数据集 MultiSports，用于检测时空定位的体育动作，提供了一组细致的标注，以便作为未来时空动作检测的标准基准。