Numerous prior studies predominantly emphasize constructing relation vectors
for individual neighborhood points and generating dynamic kernels for each
vector and embedding these into high-dimensional spaces to capture implicit
local structures. However, we contend that such implicit high-dimensional
structure modeling approch inadequately represents the local geometric
structure of point clouds due to the absence of explicit structural
information. Hence, we introduce X-3D, an explicit 3D structure modeling
approach. X-3D functions by capturing the explicit local structural information
within the input 3D space and employing it to produce dynamic kernels with
shared weights for all neighborhood points within the current local region.
This modeling approach introduces effective geometric prior and significantly
diminishes the disparity between the local structure of the embedding space and
the original input point cloud, thereby improving the extraction of local
features. Experiments show that our method can be used on a variety of methods
and achieves state-of-the-art performance on segmentation, classification,
detection tasks with lower extra computational cost, such as \textbf{90.7\%} on
ScanObjectNN for classification, \textbf{79.2\%} on S3DIS 6 fold and
\textbf{74.3\%} on S3DIS Area 5 for segmentation, \textbf{76.3\%} on ScanNetV2
for segmentation and \textbf{64.5\%} mAP , \textbf{46.9\%} mAP on SUN RGB-D and
\textbf{69.0\%} mAP , \textbf{51.1\%} mAP on ScanNetV2 . Our code is available
at
\href{https://github.com/sunshuofeng/X-3D}{this https URL}.

X-3D 是一种显式的三维结构建模方法，通过捕捉输入的三维空间中的显式局部结构信息，并结合当前局部区域中所有邻域点的共享权重产生动态核心，有效地改善了嵌入空间的局部结构与原始输入点云之间的差异，提高了局部特征的提取能力。

X-3D：点云识别的显式三维结构建模

X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition

This paper proposes a new framework for low-light image enhancement by
simultaneously conducting the appearance as well as structure modeling. It
employs the structural feature to guide the appearance enhancement, leading to
sharp and realistic results. The structure modeling in our framework is
implemented as the edge detection in low-light images. It is achieved with a
modified generative model via designing a structure-aware feature extractor and
generator. The detected edge maps can accurately emphasize the essential
structural information, and the edge prediction is robust towards the noises in
dark areas. Moreover, to improve the appearance modeling, which is implemented
with a simple U-Net, a novel structure-guided enhancement module is proposed
with structure-guided feature synthesis layers. The appearance modeling, edge
detector, and enhancement module can be trained end-to-end. The experiments are
conducted on representative datasets (sRGB and RAW domains), showing that our
model consistently achieves SOTA performance on all datasets with the same
architecture.

本文提出了一种新的低光图像增强框架，通过同时进行外观和结构建模，引入结构特征来指导外观增强，采用改进的生成模型实现结构建模，提出了结构引导增强模块来改善外观建模，实现了端到端的训练，并在代表性数据集上进行实验，表明我们的模型在所有数据集上均实现了最佳表现。

低光图像增强：结构建模和引导

Low-Light Image Enhancement via Structure Modeling and Guidance

Self-attention networks (SANs) with selective mechanism has produced
substantial improvements in various NLP tasks by concentrating on a subset of
input words. However, the underlying reasons for their strong performance have
not been well explained. In this paper, we bridge the gap by assessing the
strengths of selective SANs (SSANs), which are implemented with a flexible and
universal Gumbel-Softmax. Experimental results on several representative NLP
tasks, including natural language inference, semantic role labelling, and
machine translation, show that SSANs consistently outperform the standard SANs.
Through well-designed probing experiments, we empirically validate that the
improvement of SSANs can be attributed in part to mitigating two commonly-cited
weaknesses of SANs: word order encoding and structure modeling. Specifically,
the selective mechanism improves SANs by paying more attention to content words
that contribute to the meaning of the sentence. The code and data are released
at this https URL

本文通过评估实现灵活通用的 Gumbel-Softmax 的选择性自注意力网络（SSANs）的性能，在多个代表性 NLP 任务中，包括自然语言推理、语义角色标注和机器翻译，实验结果显示，SSANs 一致优于标准 SANs 并通过良好设计的探针实验证明，其改进性在一定程度上归因于缓解 SANs 的两个常见缺点：词序编码和结构建模，具体而言，选择机制通过更多地关注对句子含义有贡献的内容单词，提高了 SANs 的性能。