用于单视网型3D重建（SVR）的神经网络（NN）已经获得了普及。最近的工作指出，对于SVR，大多数尖端NNS在重建看不见的对象时具有有限的性能，因为它们主要依赖于识别（即，基于分类的方法）而不是形状重建。要深入了解这个问题，我们对NNS更倾向识别重建的何时以及为什么提供系统的研究，反之亦然。我们的发现表明，确定识别与重建的主要因素是如何分散训练数据。因此，我们介绍了一个新的数据驱动度量的分散评分，以量化这种前导因素并研究其对NNS的影响。我们假设当训练图像更加分散时，NNS朝向识别偏置，并且训练形状较少分散。支持我们的假设，通过我们的合成和基准数据集的实验证明了分散评分。我们表明，拟议的指标是分析重建质量的主要方法，并提供除了传统的重建分数之外的新颖信息。

我们呈现多视图姿势变压器（MVP），用于从多视图图像估计多人3D姿势。而不是从昂贵的体积表示或从多个检测到的2D重建的每人3D姿势估计从昂贵的体积表示或从多个检测到的2D姿势进行估计3D联合位置，而是MVP以清洁和有效的方式直接回归多人3D姿势，而不依赖于中间任务。具体而言，MVP表示作为学习查询嵌入的骨架关节，并让它们从输入图像中逐渐参加和原因，以直接回归实际的3D联合位置。为了提高这种简单管道的准确性，MVP呈现了一个分层方案，简明地代表了多人骨架关节的查询嵌入，并引入了输入相关的查询适应方法。此外，MVP设计了一种新颖的几何引导注意力机制，称为投影注意力，更精确地熔化每个关节的跨视网膜信息。 MVP还介绍了RAYCONV操作，以将视图依赖的相机几何整合到特征表示中，以增加投射注意。我们通过实验展示我们的MVP模型在几个基准上占据了最先进的方法，同时更有效。值得注意的是，它在挑战的Panoptic DataSet上实现了92.3％的AP25，提高了先前的最佳方法[36]，提高了9.8％。 MVP是通用的，并且还可以扩展到恢复SMPL模型表示的人网格，因此可用于建模多人身体形状。代码和模型可在https://github.com/sail-sg/mvp上获得。

图表分类具有生物信息学，社会科学，自动假新闻检测，Web文档分类等中的应用程序。在许多实践方案中，包括网络级应用程序，其中标签稀缺或难以获得，无人监督的学习是一种自然范式，但它交易表现。最近，对比学习（CL）使得无监督的计算机视觉模型能够竞争对抗监督。分析Visual CL框架的理论和实证工作发现，利用大型数据集和域名感知增强对于框架成功至关重要。有趣的是，图表CL框架通常会在使用较小数据的顺序的同时报告高性能，并且使用可能损坏图形的底层属性的域名增强（例如，节点或边缘丢弃，功能捕获）。通过这些差异的激励，我们寻求确定：（i）为什么现有的图形Cl框架尽管增加了增强和有限的数据; （ii）是否遵守Visual CL原理可以提高图形分类任务的性能。通过广泛的分析，我们识别图形数据增强和评估协议的缺陷实践，这些协议通常用于图形CL文献中，并提出了未来的研究和应用的改进的实践和理智检查。我们表明，在小型基准数据集上，图形神经网络的归纳偏差可以显着补偿现有框架的局限性。在采用相对较大的图形分类任务的研究中，我们发现常用的域名忽视增强的表现不佳，同时遵守Visual Cl中的原则可以显着提高性能。例如，在基于图形的文档分类中，可以用于更好的Web搜索，我们显示任务相关的增强提高了20％的准确性。

在节点分类任务中，异常和过天性是两个可能损害图形卷积神经网络（GCN）性能的两个问题。异种源于问题是指模型无法处理异构节点属于不同类别的异细则图;过度问题是指模型的退化性能随着越来越多的层。这两个看似无关的问题大多是独立研究的，但最近有近期解决一个问题可能有益于另一个问题的经验证据。在这项工作中，除了经验观察之外，我们的目标是：（1）从统一的理论角度分析异常和过天际上的问题，（2）确定两个问题的共同原因，（3）提出简单但有效的解决策略共同的原因。在我们的理论分析中，我们表明异通源性和过天际上问题的共同原因 - 即节点的相对程度及其异常级别 - 触发连续层中的节点表示，以“移动”更靠近原始决策边界，这增加了某些约束下节点标签的错误分类率。理论上我们显示：（1）具有高异味的节点具有更高的错误分类率。 （2）即使在异常的情况下，节点邻域中的程度差异也可以影响节点表示的运动并导致“伪异性”情况，这有助于解释过度处理。 （3）允许在消息传递期间肯定的阳性而且负面信息可以有助于抵消两个问题的常见原因。基于我们的理论见解，我们提出了对GCN架构的简单修改（即学习程度校正和签名消息），我们表明他们在9个网络上缓解了HeteOlephily和过天际上的问题。

探讨了语言建模流行的变形金刚，用于近期解决视觉任务，例如，用于图像分类的视觉变压器（VIT）。 VIT模型将每个图像分成具有固定长度的令牌序列，然后应用多个变压器层以模拟它们的全局关系以进行分类。然而，当从像想象中的中型数据集上从头开始训练时，VIT对CNNS达到较差的性能。我们发现它是因为：1）输入图像的简单标记未能模拟相邻像素之间的重要局部结构，例如边缘和线路，导致训练采样效率低。 2）冗余注意骨干骨干设计对固定计算预算和有限的训练样本有限的具有限制性。为了克服这些限制，我们提出了一种新的令牌到令牌视觉变压器（T2T-VIT），它包含1）层 - 明智的代币（T2T）转换，通过递归聚合相邻来逐步地结构于令牌到令牌。代币进入一个令牌（令牌到令牌），这样可以建模由周围令牌所代表的本地结构，并且可以减少令牌长度; 2）一种高效的骨干，具有深度狭窄的结构，用于在实证研究后CNN建筑设计的激励变压器结构。值得注意的是，T2T-VIT将Vanilla Vit的参数计数和Mac减少了一半，同时从想象中从头开始训练时，改善了超过3.0 \％。它还优于Endnets并通过直接培训Imagenet训练来实现与MobileNets相当的性能。例如，T2T-VTO与Reset50（21.5M参数）的可比大小（21.5M参数）可以在图像分辨率384 $ \ Times 384上实现83.3 \％TOP1精度。 （代码：https://github.com/yitu-opensource/t2t-vit）

We investigate the representation power of graph neural networks in the semisupervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure (e.g., multilayer perceptrons). Motivated by this limitation, we identify a set of key designs-ego-and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations-that boost learning from the graph structure under heterophily. We combine them into a graph neural network, H 2 GCN, which we use as the base method to empirically evaluate the effectiveness of the identified designs. Going beyond the traditional benchmarks with strong homophily, our empirical analysis shows that the identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively, and yield competitive performance under homophily.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.