体育视频分析是由于各种应用领域的普遍研究课题，从多媒体智能设备带来了用户量身定制的易消化，以分析运动员的表现。体育视频任务是Mediaeval 2021基准测试的一部分。此任务可以从视频中解决细粒度的动作检测和分类。重点是乒乓球比赛的录音。自2019年以来运行，该任务从未在自然条件下录制的未经监测视频提供了分类挑战，每个行程都有已知的时间边界。今年，数据集延长并提供了未经注释的未经监测视频的检测挑战。这项工作旨在为体育教练和玩家创造工具，以分析体育绩效。在这种技术可以建立运动分析和玩家分析，以丰富运动员的培训经验，提高他们的表现。

We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the perception head. The intuition is that if the network is able to reconstruct the scene surface, given only sparse input points, then it probably also captures some fragments of semantic information, that can be used to boost an actual perception task. This principle has a very simple formulation, which makes it both easy to implement and widely applicable to a large range of 3D sensors and deep networks performing semantic segmentation or object detection. In fact, it supports a single-stream pipeline, as opposed to most contrastive learning approaches, allowing training on limited resources. We conducted extensive experiments on various autonomous driving datasets, involving very different kinds of lidars, for both semantic segmentation and object detection. The results show the effectiveness of our method to learn useful representations without any annotation, compared to existing approaches. Code is available at \href{https://github.com/valeoai/ALSO}{github.com/valeoai/ALSO}

Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, semi-supervised object detection methods, which consist in leveraging abundant unlabeled data, have been proposed and have already shown impressive results. However, most of these methods require linking a pseudo-label to a ground-truth object by thresholding. In previous works, this threshold value is usually determined empirically, which is time consuming, and only done for a single data distribution. When the domain, and thus the data distribution, changes, a new and costly parameter search is necessary. In this work, we introduce our method Adaptive Self-Training for Object Detection (ASTOD), which is a simple yet effective teacher-student method. ASTOD determines without cost a threshold value based directly on the ground value of the score histogram. To improve the quality of the teacher predictions, we also propose a novel pseudo-labeling procedure. We use different views of the unlabeled images during the pseudo-labeling step to reduce the number of missed predictions and thus obtain better candidate labels. Our teacher and our student are trained separately, and our method can be used in an iterative fashion by replacing the teacher by the student. On the MS-COCO dataset, our method consistently performs favorably against state-of-the-art methods that do not require a threshold parameter, and shows competitive results with methods that require a parameter sweep search. Additional experiments with respect to a supervised baseline on the DIOR dataset containing satellite images lead to similar conclusions, and prove that it is possible to adapt the score threshold automatically in self-training, regardless of the data distribution.

我们为视频中的无监督对象细分提出了一种简单而强大的方法。我们引入了一个目标函数，其最小值代表输入序列上主要显着对象的掩码。它仅依赖于独立的图像特征和光流，可以使用现成的自我监督方法获得。它以序列的长度缩放，不需要超级像素或稀疏，并且在没有任何特定培训的情况下将其推广到不同的数据集。该目标函数实际上可以从应用于整个视频的光谱群集形式得出。我们的方法通过标准基准（Davis2016，segtrack-v2，fbms59）实现了PAR的性能，同时在概念上且实际上更简单。代码可从https://ponimatkin.github.io/ssl-vos获得。

接受经验风险最小化（ERM）训练的机器学习模型的预测性能可以大大降解分配变化。在训练数据集中存在虚假相关性的存在导致ERM训练的模型在对不存在此类相关性的少数群体评估时表现出很高的损失。已经进行了广泛的尝试来开发改善最差的鲁棒性的方法。但是，他们需要每个培训输入的组信息，或者至少需要一个带有组标签的验证设置来调整其超参数，这可能是昂贵的或未知的。在本文中，我们应对在培训或验证期间没有小组注释的情况下提高组鲁棒性的挑战。为此，我们建议根据``识别''模型提取的特征的革兰氏集矩阵将训练数据集分为组，并根据这些伪组应用强大的优化。在不可用的小组标签的现实情况下，我们的实验表明，我们的方法不仅可以改善对ERM的稳健性，而且还优于所有最近的基线

我们提出了Panohdr-nerf，这是一种新颖的管道，可随意捕获大型室内场景的合理的全HDR辐射场，而无需精心设计或复杂的捕获协议。首先，用户通过在场景中自由挥舞现成的摄像头来捕获场景的低动态范围（LDR）全向视频。然后，LDR2HDR网络将捕获的LDR帧提升到HDR，随后用于训练定制的NERF ++模型。由此产生的Panohdr-NERF管道可以从场景的任何位置估算完整的HDR全景。通过在一个新的测试数据集上进行各种真实场景的实验，并在训练过程中未见的位置捕获了地面真相HDR辐射，我们表明PanoHDR-NERF可以预测任何场景点的合理辐射。我们还表明，PanoHDR-NERF产生的HDR图像可以合成正确的照明效果，从而可以使用正确点亮的合成对象来增强室内场景。

隐式神经网络已成功用于点云的表面重建。然而，它们中的许多人面临着可扩展性问题，因为它们将整个对象或场景的异构面功能编码为单个潜在载体。为了克服这种限制，一些方法在粗略普通的3D网格或3D补丁上推断潜伏向量，并将它们插入以应对占用查询。在这样做时，它们可以与对象表面上采样的输入点进行直接连接，并且它们在空间中均匀地附加信息，而不是其最重要的信息，即在表面附近。此外，依赖于固定的补丁大小可能需要离散化调整。要解决这些问题，我们建议使用点云卷积并计算每个输入点的潜伏向量。然后，我们使用推断的权重在最近的邻居上执行基于学习的插值。对象和场景数据集的实验表明，我们的方法在大多数古典指标上显着优于其他方法，产生更精细的细节和更好的重建更薄的卷。代码可在https://github.com/valeoai/poco获得。

最近对隐含形状表示的兴趣日益增长。与明确的陈述相反，他们没有解决局限性，他们很容易处理各种各样的表面拓扑。为了了解这些隐式表示，电流方法依赖于一定程度的形状监督（例如，内部/外部信息或距离形状知识），或者至少需要密集点云（以近似距离 - 到 - 到 - 形状）。相比之下，我们介绍{\方法}，一种用于学习形状表示的自我监督方法，从可能极其稀疏的点云。就像在水牛的针问题一样，我们在点云上“掉落”（样本）针头，认为，静统计地靠近表面，针端点位于表面的相对侧。不需要形状知识，点云可以高稀疏，例如，作为车辆获取的Lidar点云。以前的自我监督形状表示方法未能在这种数据上产生良好的结果。我们获得定量结果与现有的形状重建数据集上现有的监督方法标准，并在Kitti等硬自动驾驶数据集中显示有前途的定性结果。

虽然对2D图像的零射击学习（ZSL）进行了许多研究，但其在3D数据中的应用仍然是最近且稀缺的，只有几种方法限于分类。我们在3D数据上介绍了ZSL和广义ZSL（GZSL）的第一代生成方法，可以处理分类，并且是第一次语义分割。我们表明它达到或胜过了INTEMNET40对归纳ZSL和归纳GZSL的ModelNet40分类的最新状态。对于语义分割，我们创建了三个基准，用于评估此新ZSL任务，使用S3DIS，Scannet和Semantickitti进行评估。我们的实验表明，我们的方法优于强大的基线，我们另外为此任务提出。

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks.