To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.

了解动态场景中的3D运动对于许多视觉应用至关重要。最近的进步主要集中在估计人类等某些特定元素的活动上。在本文中，我们利用神经运动场来估计多视图设置中所有点的运动。由于颜色相似的点和与时变颜色的点的歧义，从动态场景中对动态场景进行建模运动是具有挑战性的。我们建议将估计运动的正规化为可预测。如果已知来自以前的帧的运动，那么在不久的将来的运动应该是可以预测的。因此，我们通过首先调节潜在嵌入的估计运动来引入可预测性正则化，然后通过采用预测网络来在嵌入式上执行可预测性。所提出的框架pref（可预测性正则化字段）比基于最先进的神经运动场的动态场景表示方法在PAR或更好的结果上取得了更好的成绩，同时不需要对场景的先验知识。

在本文中，我们为复杂场景进行了高效且强大的深度学习解决方案。在我们的方法中，3D场景表示为光场，即，一组光线，每组在到达图像平面时具有相应的颜色。对于高效的新颖视图渲染，我们采用了光场的双面参数化，其中每个光线的特征在于4D参数。然后，我们将光场配向作为4D函数，即将4D坐标映射到相应的颜色值。我们训练一个深度完全连接的网络以优化这种隐式功能并记住3D场景。然后，特定于场景的模型用于综合新颖视图。与以前需要密集的视野的方法不同，需要密集的视野采样来可靠地呈现新颖的视图，我们的方法可以通过采样光线来呈现新颖的视图并直接从网络查询每种光线的颜色，从而使高质量的灯场呈现稀疏集合训练图像。网络可以可选地预测每光深度，从而使诸如自动重新焦点的应用。我们的小说视图合成结果与最先进的综合结果相当，甚至在一些具有折射和反射的具有挑战性的场景中优越。我们在保持交互式帧速率和小的内存占地面积的同时实现这一点。

Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of existing methods. In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC). Then, we derive an effective three-stage iterative solution for the proposed model and provide analytic solutions for the three sub-problems from the three stages. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features. Extensive experiments on three benchmark data sets demonstrate the effectiveness of the proposed method.

现有的视频域改编（DA）方法需要存储视频帧的所有时间组合或配对源和目标视频，这些视频和目标视频成本昂贵，无法扩展到长时间的视频。为了解决这些局限性，我们建议采用以下记忆高效的基于图形的视频DA方法。首先，我们的方法模型每个源或目标视频通过图：节点表示视频帧和边缘表示帧之间的时间或视觉相似性关系。我们使用图形注意力网络来了解单个帧的重量，并同时将源和目标视频对齐到域不变的图形特征空间中。我们的方法没有存储大量的子视频，而是仅构建一个图形，其中一个视频的图形注意机制，从而大大降低了内存成本。广泛的实验表明，与最先进的方法相比，我们在降低内存成本的同时取得了卓越的性能。

由于研究和应用意义，人类行动认可在近年来造成了很多关注。行动识别的大多数现有工程侧重于学习视频的有效空间特征，但忽视了前提，行动和效果之间的强烈因果关系。这种关系对动作识别的准确性来说也是至关重要的。在本文中，我们建议根据前提条件和效果模拟因果关系，以提高行动识别性能。具体地，提出了一种循环推理模型来捕获动作识别的因果关系。为此，我们向大规模动作数据集注释了前提条件和效果。实验结果表明，所提出的循环推理模型可以有效地推理前提和效果，可以提高行动识别性能。

当通过相机或图片与对象交互时，用户通常具有特定的意图。例如，他们可能想执行视觉搜索。由于大多数对象检测模型依靠图像像素作为其唯一输入，因此不希望的结果并不少见。最常见的是：缺乏对感兴趣的对象的高信心检测，或使用错误的类标签检测。当操作容量受限的移动对象检测器上设置时，问题尤其严重。在本文中，我们调查了调节移动检测器的技术，以明确说明用户意图，以简单查询的嵌入表示。与标准探测器相比，查询调制的检测器在检测给定用户查询的对象时表现出卓越的性能。得益于从标准对象检测注释中合成的大规模训练数据，查询调制的检测器也表现出色的转介表达识别系统。还可以对查询调制的检测器进行培训，以同时解决本地化用户查询和标准检测的求解，即使在规范可可任务上甚至超过了标准移动检测器。

We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces. Nearest neighbors of points on a set of randomly selected rays are recorded into a tensor, called the RaySense signature. From the signature, statistical information about the data set, as well as certain geometrical information, can be extracted, independent of the ray set. We present a few examples illustrating applications of the proposed sampling strategy.

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.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.