Recently, vision transformers have performed well in various computer vision
tasks, including voxel 3D reconstruction. However, the windows of the vision
transformer are not multi-scale, and there is no connection between the
windows, which limits the accuracy of voxel 3D reconstruction . Therefore, we
propose a shifted windows attention voxel 3D reconstruction network. To the
best of our knowledge, this is the first work to apply shifted window attention
to voxel 3D reconstruction. Experimental results on ShapeNet verify our method
achieves SOTA accuracy in single-view reconstruction.

最近，视觉变换器在各种计算机视觉任务中表现出色，包括体素三维重建。然而，视觉变换器的窗口不是多尺度的，并且窗口之间没有连接，这限制了体素三维重建的准确性。因此，我们提出了一种移位窗口注意力体素三维重建网络。据我们所知，这是第一个将移位窗口注意力应用于体素三维重建的工作。在 ShapeNet 上的实验结果验证了我们的方法在单视图重建方面达到了 SOTA 准确性。

R3D-SWIN：应用移位窗口注意力进行单视角三维重建

R3D-SWIN:Use Shifted Window Attention for Single-View 3D Reconstruction

Despite the technological advancements in the construction and surveying
sector, the inspection of salient features like windows in an
under-construction or existing building is predominantly a manual process.
Moreover, the number of windows present in a building is directly related to
the magnitude of deformation it suffers under earthquakes. In this research, a
method to accurately detect and count the number of windows of a building by
deploying an Unmanned Aerial Vehicle (UAV) based remote sensing system is
proposed. The proposed two-stage method automates the identification and
counting of windows by developing computer vision pipelines that utilize data
from UAV's onboard camera and other sensors. Quantitative and Qualitative
results show the effectiveness of our proposed approach in accurately detecting
and counting the windows compared to the existing method.

本研究提出了一种利用无人机遥感系统来准确检测和计数建筑物窗户数量的方法，该方法通过开发计算机视觉流水线，利用无人机机载摄像头和其他传感器的数据，实现了窗户的自动识别和计数。定量和定性结果表明，与现有方法相比，我们提出的方法能够准确检测和计数窗户。

基于无人机图像的遥感技术自动检测和计数窗户

Automated Detection and Counting of Windows using UAV Imagery based  Remote Sensing

Differentiating malware is important to determine their behaviors and level
of threat; as well as to devise defensive strategy against them. In response,
various anti-malware systems have been developed to distinguish between
different malwares. However, most of the recent malware families are Artificial
Intelligence (AI) enable and can deceive traditional anti-malware systems using
different obfuscation techniques. Therefore, only AI-enabled anti-malware
system is robust against these techniques and can detect different features in
the malware files that aid in malicious activities. In this study we review two
AI-enabled techniques for detecting malware in Windows and Android operating
system, respectively. Both the techniques achieved perfect accuracy in
detecting various malware families.

该研究评估了两种基于人工智能技术的恶意软件检测技术，分别适用于 Windows 和 Android 操作系统，结果表明这两种技术均可高度精确地检测出不同家族的恶意软件。