Infrared small target detection (IRSTD) has recently benefitted greatly from
U-shaped neural models. However, largely overlooking effective global
information modeling, existing techniques struggle when the target has high
similarities with the background. We present a Spatial-channel Cross
Transformer Network (SCTransNet) that leverages spatial-channel cross
transformer blocks (SCTBs) on top of long-range skip connections to address the
aforementioned challenge. In the proposed SCTBs, the outputs of all encoders
are interacted with cross transformer to generate mixed features, which are
redistributed to all decoders to effectively reinforce semantic differences
between the target and clutter at full scales. Specifically, SCTB contains the
following two key elements: (a) spatial-embedded single-head channel-cross
attention (SSCA) for exchanging local spatial features and full-level global
channel information to eliminate ambiguity among the encoders and facilitate
high-level semantic associations of the images, and (b) a complementary
feed-forward network (CFN) for enhancing the feature discriminability via a
multi-scale strategy and cross-spatial-channel information interaction to
promote beneficial information transfer. Our SCTransNet effectively encodes the
semantic differences between targets and backgrounds to boost its internal
representation for detecting small infrared targets accurately. Extensive
experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1k,
demonstrate that the proposed SCTransNet outperforms existing IRSTD methods.
Our code will be made public at this https URL

提出了一种基于空间通道交叉变换器网络（SCTransNet）的红外小目标检测方法，利用跨通道变换器块（SCTBs）以及长程跳跃连接来加强目标和背景之间的语义差异，有效地检测小尺度的红外目标。

SCTransNet：红外小目标检测的空间 - 通道交叉变换网络

SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small  Target Detection

With the vigorous development of the urban construction industry, engineering
deformation or changes often occur during the construction process. To combat
this phenomenon, it is necessary to detect changes in order to detect
construction loopholes in time, ensure the integrity of the project and reduce
labor costs. Or the inconvenience and injuriousness of the road. In the study
of change detection in 3D point clouds, researchers have published various
research methods on 3D point clouds. Directly based on but mostly based
ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to
convert 3D point clouds into DSM, which loses a lot of original information.
Although deep learning is used in remote sensing methods, in terms of change
detection of 3D point clouds, it is more converted into two-dimensional
patches, and neural networks are rarely applied directly. We prefer that the
network is given at the level of pixels or points. Variety. Therefore, in this
article, our network builds a network for 3D point cloud change detection, and
proposes a new module Cross transformer suitable for change detection.
Simultaneously simulate tunneling data for change detection, and do test
experiments with our network.

在这篇论文中，我们利用 3D 点云构建了一个适用于变化检测的网络，并提出了一种新的适用于变化检测的模块 Cross transformer。同时，我们使用我们的网络对隧道数据进行变化检测的测试实验。

基于自交叉 Transformer 模型的点云变化检测器研究

Research on self-cross transformer model of point cloud change detecter

The rapid development of point cloud learning has driven point cloud
completion into a new era. However, the information flows of most existing
completion methods are solely feedforward, and high-level information is rarely
reused to improve low-level feature learning. To this end, we propose a novel
Feedback Network (FBNet) for point cloud completion, in which present features
are efficiently refined by rerouting subsequent fine-grained ones. Firstly,
partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to
generate coarse shapes. Then, we cascade several Feedback-Aware Completion
(FBAC) Blocks and unfold them across time recurrently. Feedback connections
between two adjacent time steps exploit fine-grained features to improve
present shape generations. The main challenge of building feedback connections
is the dimension mismatching between present and subsequent features. To
address this, the elaborately designed point Cross Transformer exploits
efficient information from feedback features via cross attention strategy and
then refines present features with the enhanced feedback features. Quantitative
and qualitative experiments on several datasets demonstrate the superiority of
proposed FBNet compared to state-of-the-art methods on point completion task.

本文提出了一种新的反馈网络（FBNet）用于点云完整性修复，通过用细粒度特征改进粗略形状的生成，并利用交叉注意力策略来修正现有的特征。在多个数据集上进行的定量和定性实验表明，FBNet 在点云修复任务上的性能比现有最先进的方法更优。