Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction, BEV feature construction, and task heads. Among the three components, BEV feature construction is BEV-specific compared with 2D tasks. Existing methods aggregate the multi-view camera features to the flattened grid in order to construct the BEV feature. However, flattening the BEV space along the height dimension fails to emphasize the informative features of different heights. For example, the barrier is located at a low height while the truck is located at a high height. In this paper, we propose a novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the intrinsic characteristics of different heights. Instead of flattening the BEV space, we first sample along the height dimension to build the global and local BEV slices. Then, the features of BEV slices are aggregated from the camera features and merged by the attention mechanism. Finally, we fuse the merged local and global BEV features by a transformer to generate the final feature map for task heads. The purpose of local BEV slices is to emphasize informative heights. In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices. Compared with uniform sampling, LiDAR-guided sampling can determine more informative heights. We conduct detailed experiments to demonstrate the effectiveness of BEV-SAN. Code will be released.

Recently, Bird's-Eye-View (BEV) representation has gained increasing attention in multi-view 3D object detection, which has demonstrated promising applications in autonomous driving. Although multi-view camera systems can be deployed at low cost, the lack of depth information makes current approaches adopt large models for good performance. Therefore, it is essential to improve the efficiency of BEV 3D object detection. Knowledge Distillation (KD) is one of the most practical techniques to train efficient yet accurate models. However, BEV KD is still under-explored to the best of our knowledge. Different from image classification tasks, BEV 3D object detection approaches are more complicated and consist of several components. In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner. However, directly applying the teacher-student paradigm to BEV features fails to achieve satisfying results due to heavy background information in RGB cameras. To solve this problem, we propose to leverage the localization advantage of LiDAR points. Specifically, we transform the LiDAR points to BEV space and generate the foreground mask and view-dependent mask for the teacher-student paradigm. It is to be noted that our method only uses LiDAR points to guide the KD between RGB models. As the quality of depth estimation is crucial for BEV perception, we further introduce depth distillation to our framework. Our unified framework is simple yet effective and achieves a significant performance boost. Code will be released.

Vision-Centric Bird-Eye-View (BEV) perception has shown promising potential and attracted increasing attention in autonomous driving. Recent works mainly focus on improving efficiency or accuracy but neglect the domain shift problem, resulting in severe degradation of transfer performance. With extensive observations, we figure out the significant domain gaps existing in the scene, weather, and day-night changing scenarios and make the first attempt to solve the domain adaption problem for multi-view 3D object detection. Since BEV perception approaches are usually complicated and contain several components, the domain shift accumulation on multi-latent spaces makes BEV domain adaptation challenging. In this paper, we propose a novel Multi-level Multi-space Alignment Teacher-Student ($M^{2}ATS$) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Multi-space Feature Aligned (MFA) student model. Specifically, DAT model adopts uncertainty guidance to sample reliable depth information in target domain. After constructing domain-invariant BEV perception, it then transfers pixel and instance-level knowledge to student model. To further alleviate the domain shift at the global level, MFA student model is introduced to align task-relevant multi-space features of two domains. To verify the effectiveness of $M^{2}ATS$, we conduct BEV 3D object detection experiments on four cross domain scenarios and achieve state-of-the-art performance (e.g., +12.6% NDS and +9.1% mAP on Day-Night). Code and dataset will be released.

我们提出了CrossHuman，这是一种新颖的方法，该方法从参数人类模型和多帧RGB图像中学习了交叉指导，以实现高质量的3D人类重建。为了恢复几何细节和纹理，即使在无形区域中，我们设计了一个重建管道，结合了基于跟踪的方法和无跟踪方法。给定一个单眼RGB序列，我们在整个序列中跟踪参数人模型，与目标框架相对应的点（体素）被参数体运动扭曲为参考框架。在参数体的几何学先验和RGB序列的空间对齐特征的指导下，稳健隐式表面被融合。此外，将多帧变压器（MFT）和一个自我监管的经过修补模块集成到框架中，以放宽参数主体的要求并帮助处理非常松散的布。与以前的作品相比，我们的十字人类可以在可见的和无形区域启用高保真的几何细节和纹理，并提高人类重建的准确性，即使在估计的不准确的参数人类模型下也是如此。实验表明我们的方法达到了最新的（SOTA）性能。

最近，深层神经网络（DNNS）用于减少带宽并提高互联网视频交付的质量。现有的方法训练服务器上每个视频块的相应内容超级分辨率（SR）模型，并将低分辨率（LR）视频块以及SR模型一起流到客户端。尽管他们取得了令人鼓舞的结果，但网络培训的巨大计算成本限制了其实际应用。在本文中，我们提出了一种名为有效元调整（EMT）的方法，以降低计算成本。 EMT没有从头开始训练，而是将元学习的模型适应了输入视频的第一部分。至于以下块，它通过以前的改编模型的梯度掩盖选择了部分参数。为了实现EMT的进一步加速，我们提出了一种新颖的抽样策略，以从视频帧中提取最具挑战性的补丁。拟议的策略高效，带来了可忽略的额外成本。我们的方法大大降低了计算成本并取得更好的性能，为将神经视频传递技术应用于实际应用铺平了道路。我们基于各种有效的SR架构进行了广泛的实验，包括ESPCN，SRCNN，FSRCNN和EDSR-1，证明了我们工作的概括能力。该代码通过\ url {https://github.com/neural-video-delivery/emt-pytorch-eccv2022}发布。

具有大尺度图像文本对的视觉预训练（VLP）在各个领域都表现出卓越的性能。但是，Internet上的图像文本对共存通常缺乏明确的对齐信息，这对于VLP来说是次优的。建议采用现成的对象检测器来利用其他图像标签信息。但是，对象检测器是耗时的，只能识别预定义的对象类别，从而限制了模型容量。受到观察的启发，即文本包含不完整的细粒图像信息，我们介绍了Ideas，该想法代表通过在线多标签识别VLP来增加文本多样性。想法表明，可以在VLP期间共同优化从文本中提取的图像标签的多标签学习。此外，想法可以在线识别有价值的图像标签，以提供更明确的文本监督。全面的实验表明，想法可以显着提高多个下游数据集上的性能，并具有较小的额外计算成本。

混合样品正则化（MSR），例如混合或cutmix，是一种强大的数据增强策略，可以推广卷积神经网络。先前的经验分析说明了MSR与传统的离线知识蒸馏（KD）之间的正交性能增长。更具体地说，可以通过MSR参与顺序蒸馏的训练阶段来增强学生网络。然而，MSR和在线知识蒸馏之间的相互作用，这是一个更强的蒸馏范式，在那里，一群同伴互相学习的合奏仍然没有探索。为了弥合差距，我们首次尝试将cutmix纳入在线蒸馏中，我们从经验上观察到了重大改进。在这个事实的鼓舞下，我们提出了一个更强大的MSR，专门用于在线蒸馏，称为Cut^nMix。此外，一个新颖的在线蒸馏框架是在切割^nmix上设计的，以通过功能水平相互学习和自我启动的老师来增强蒸馏。对CIFAR10和CIFAR100进行六个网络体系结构的全面评估表明，我们的方法可以始终超过最先进的蒸馏方法。

我们提出了一个称为Banet的运动预测模型，该模型意味着边界感知网络，它是LaneGCN的变体。我们认为，仅使用车道中心线作为输入不足以获得矢量图节点的嵌入功能。车道中心线只能提供车道的拓扑，而矢量图的其他元素还包含丰富的信息。例如，车道边界可以提供流量规则约束信息，例如是否可以更改车道，这非常重要。因此，我们通过在运动预测模型中编码更多的向量图元素来实现更好的性能。我们报告了2022年AgroverSe2运动预测挑战的结果，并在测试排行榜上排名第二。

由于计算的未来是异质的，因此可伸缩性是单图超分辨率的关键问题。最近的工作尝试训练一个网络，该网络可以部署在具有不同能力的平台上。但是，他们依靠像素稀疏卷积，这不是硬件友好，并且实现了有限的实际加速。由于可以将图像分为各种恢复困难的斑块，因此我们提出了一种基于自适应贴片（APE）的可扩展方法，以实现更实用的加速。具体而言，我们建议训练回归器，以预测贴片每一层的增量能力。一旦增量容量低于阈值，贴片就可以在特定层中退出。我们的方法可以通过改变增量容量的阈值来轻松调整性能和效率之间的权衡。此外，我们提出了一种新的策略，以实现我们方法的网络培训。我们在各种骨架，数据集和缩放因素上进行了广泛的实验，以证明我们方法的优势。代码可从https://github.com/littlepure2333/ape获得