The Unmanned Aerial Vehicles (UAVs) market has been significantly growing and
Considering the availability of drones at low-cost prices the possibility of
misusing them, for illegal purposes such as drug trafficking, spying, and
terrorist attacks posing high risks to national security, is rising. Therefore,
detecting and tracking unauthorized drones to prevent future attacks that
threaten lives, facilities, and security, become a necessity. Drone detection
can be performed using different sensors, while image-based detection is one of
them due to the development of artificial intelligence techniques. However,
knowing unauthorized drone types is one of the challenges due to the lack of
drone types datasets. For that, in this paper, we provide a dataset of various
drones as well as a comparison of recognized object detection models on the
proposed dataset including YOLO algorithms with their different versions, like,
v3, v4, and v5 along with the Detectronv2. The experimental results of
different models are provided along with a description of each method. The
collected dataset can be found in
this https URL

通过开发人工智能技术，本研究提供了一个包含不同类型未经授权的无人机数据集，用于比较各种被识别的目标检测模型，并提供了实验结果和方法描述。

无人机类型集：用于无人机检测和追踪的无人机类型检测基准

Drone-type-Set: Drone types detection benchmark for drone detection and  tracking

Product descriptions in e-commerce platforms contain detailed and valuable
information about retailers assortment. In particular, coding promotions within
digital leaflets are of great interest in e-commerce as they capture the
attention of consumers by showing regular promotions for different products.
However, this information is embedded into images, making it difficult to
extract and process for downstream tasks. In this paper, we present an
end-to-end approach that classifies promotions within digital leaflets into
their corresponding product categories using both visual and textual
information. Our approach can be divided into three key components: 1) region
detection, 2) text recognition and 3) text classification. In many cases, a
single promotion refers to multiple product categories, so we introduce a
multi-label objective in the classification head. We demonstrate the
effectiveness of our approach for two separated tasks: 1) image-based detection
of the descriptions for each individual promotion and 2) multi-label
classification of the product categories using the text from the product
descriptions. We train and evaluate our models using a private dataset composed
of images from digital leaflets obtained by Nielsen. Results show that we
consistently outperform the proposed baseline by a large margin in all the
experiments.

本文提出了一种利用图像和文本信息识别电子传单内的促销品，对其进行分类以及多标签文本分类的方法，通过对 Nielsen 电子传单获取的图像进行训练和评估，不断优化后我们的模型在所有实验中均明显优于基准模型。

基于文本和视觉信息的数字传单促销多标签分类

Multi-label classification of promotions in digital leaflets using  textual and visual information

Face recognition research is one of the most active topics in computer vision
(CV), and deep neural networks (DNN) are now filling the gap between
human-level and computer-driven performance levels in face verification
algorithms. However, although the performance gap appears to be narrowing in
terms of accuracy-based expectations, a curious question has arisen;
specifically, "Face understanding of AI is really close to that of human?" In
the present study, in an effort to confirm the brain-driven concept, we conduct
image-based detection, classification, and generation using an in-house created
fake face database. This database has two configurations: (i) false positive
face detections produced using both the Viola Jones (VJ) method and
convolutional neural networks (CNN), and (ii) simulacra that have fundamental
characteristics that resemble faces but are completely artificial. The results
show a level of suggestive knowledge that indicates the continuing existence of
a gap between the capabilities of recent vision-based face recognition
algorithms and human-level performance. On a positive note, however, we have
obtained knowledge that will advance the progress of face-understanding models.

本研究使用自行创建的虚假人脸数据库进行图像侦测、分类和生成，结果表明视觉基于人脸识别算法与人类水平的表现之间存在差距，但这为提高人脸识别算法的进展提供了可借鉴的知识。