Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG.

最近，视觉变压器及其变体在人类和多视图人类姿势估计中均起着越来越重要的作用。将图像补丁视为令牌，变形金刚可以对整个图像中的全局依赖项进行建模或其他视图中的图像。但是，全球关注在计算上是昂贵的。结果，很难将这些基于变压器的方法扩展到高分辨率特征和许多视图。在本文中，我们提出了代币螺旋的姿势变压器（PPT）进行2D人姿势估计，该姿势估计可以找到粗糙的人掩模，并且只能在选定的令牌内进行自我注意。此外，我们将PPT扩展到多视图人类姿势估计。我们建立在PPT的基础上，提出了一种新的跨视图融合策略，称为人类区域融合，该策略将所有人类前景像素视为相应的候选者。可可和MPII的实验结果表明，我们的PPT可以在减少计算的同时匹配以前的姿势变压器方法的准确性。此外，对人类360万和滑雪姿势的实验表明，我们的多视图PPT可以有效地从多个视图中融合线索并获得新的最新结果。

视觉变压器（VIT）正在出现，并且在计算机视觉任务中的准确性显着提高。但是，它们的复杂架构和巨大的计算/存储需求对新硬件加速器设计方法施加了紧迫的需求。这项工作提出了基于提议的混合速度量化的FPGA感知自动VIT加速框架。据我们所知，这是探索模型量化的第一个基于FPGA的VIT加速框架。与最先进的VIT量化工作（仅无硬件加速的算法方法）相比，我们的量化在相同的位宽度下可实现0.47％至1.36％的TOP-1精度。与32位浮点基线FPGA加速器相比，我们的加速器在框架速率上的提高约为5.6倍（即56.8 fps vs. 10.0 fps），对于DeitBase的ImagEnet数据集，精度下降了0.71％。

变形金刚在NLP和计算机视觉上实现了突破，最近开始在自动驾驶汽车（AV）的轨迹预测中表现出有希望的表现。如何有效地对自我代理与其他道路和动态对象之间的交互关系建模仍然对标准注意模块仍然具有挑战性。在这项工作中，我们提出了一个类似变压器的架构模块MNM网络，该网络配备了新型掩盖的目标调节训练程序，用于AV轨迹预测。最终的模型名为高尔夫球手，取得了最先进的性能，在2022 Waymo Open DataSet Motion Predict挑战中赢得了第二名，并根据Minade排名第一。

图形神经网络（GNN）在许多基于图的任务中表现出强大的表示能力。具体而言，由于其简单性和性能优势，GNN（例如APPNP）的解耦结构变得流行。但是，这些GNN的端到端培训使它们在计算和记忆消耗方面效率低下。为了应对这些局限性，在这项工作中，我们为图形神经网络提供了交替的优化框架，不需要端到端培训。在不同设置下进行的广泛实验表明，所提出的算法的性能与现有的最新算法相当，但具有更好的计算和记忆效率。此外，我们表明我们的框架可以利用优势来增强现有的脱钩GNN。

我们为姿势传输任务提供了一种定制的3D网格变压器模型。随着3D姿势转移基本上是依赖于给定网格的变形过程，这项工作的直觉是在具有强大的自我关注机制之间感知给定网格之间的几何不一致。具体而言，我们提出了一种新的几何对比变压器，其具有高效的3D结构感知能力，对给定网格的全局几何不一致。此外，在本地，进一步提出了一种简单但高效的中央测地对比损失，以改善区域几何不一致学习。最后，我们将潜在的等距正则化模块与新的半合成数据集一起呈现，用于跨DataSet 3D姿势传输任务对未知空间。大规模的实验结果证明了我们对SMPL-NPT，浮点和新建议的数据集SMG-3D数据集的最新定量表演的效果，以及在MG布和SMAL数据集中有前途的定性结果。结果证明，我们的方法可以实现鲁棒3D姿势传输，并且广泛地挑战来自跨数据集任务的未知空间的网格。代码和数据集可用。代码可用：https://github.com/mikecheninoulu/cgt。

估计每个视图中的2D人类姿势通常是校准多视图3D姿势估计的第一步。但是，2D姿势探测器的性能遭受挑战性的情况，例如闭塞和斜视角。为了解决这些挑战，以前的作品从eMipolar几何中的不同视图之间导出点对点对应关系，并利用对应关系来合并预测热插拔或特征表示。除了后预测合并/校准之外，我们引入了用于多视图3D姿势估计的变压器框架，其目的地通过将来自不同视图的信息集成信息来直接改善单个2D预测器。灵感来自先前的多模态变压器，我们设计一个统一的变压器体系结构，命名为输送，从当前视图和邻近视图中保险。此外，我们提出了eMipolar字段的概念来将3D位置信息编码到变压器模型中。由Epipolar字段引导的3D位置编码提供了一种有效的方式来编码不同视图的像素之间的对应关系。人类3.6M和滑雪姿势的实验表明，与其他融合方法相比，我们的方法更有效，并且具有一致的改进。具体而言，我们在256 x 256分辨率上只有5米参数达到人类3.6米的25.8毫米MPJPE。

As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.