LIDAR点云通常通过连续旋转LIDAR传感器扫描，捕获周围环境的精确几何形状，并且对于许多自主检测和导航任务至关重要。尽管已经开发了许多3D深度体系结构，但是在分析和理解点云数据中，有效收集和大量点云的注释仍然是一个主要挑战。本文介绍了Polarmix，这是一种简单且通用的点云增强技术，但可以在不同的感知任务和场景中有效地减轻数据约束。 Polarmix通过两种跨扫描扩展策略来富含点云分布，并保留点云保真度，这些杂志沿扫描方向切割，编辑和混合点云。第一个是场景级交换，它交换了两个LiDAR扫描的点云扇区，这些扫描沿方位角轴切割。第二个是实例级旋转和粘贴，它是从一个激光雷达扫描中进行的点点实例，用多个角度旋转它们（以创建多个副本），然后将旋转点实例粘贴到其他扫描中。广泛的实验表明，Polarmix在不同的感知任务和场景中始终如一地达到卓越的性能。此外，它可以用作各种3D深度体系结构的插件，并且对于无监督的域适应性也很好。

通过将元学习纳入基于区域的检测框架中，很少有射击对象检测经过广泛的研究。尽管取得了成功，但所述范式仍然受到几个因素的限制，例如（i）新型类别的低质量区域建议以及（ii）不同类别之间的类间相关性的过失。这种限制阻碍了基础知识的概括，以检测新型级别对象。在这项工作中，我们设计了元数据，（i）是第一个图像级的少量检测器，（ii）引入了一种新颖的类间相关元学习策略，以捕获和利用不同类别之间的相关性的相关性稳健而准确的几个射击对象检测。 meta-detr完全在图像级别工作，没有任何区域建议，这规避了普遍的几杆检测框架中不准确的建议的约束。此外，引入的相关元学习使元数据能够同时参加单个进料中的多个支持类别，从而可以捕获不同类别之间的类间相关性，从而大大降低了相似类别的错误分类并增强知识概括性参加新颖的课程。对多个射击对象检测基准进行的实验表明，所提出的元元删除优于大幅度的最先进方法。实施代码可在https://github.com/zhanggongjie/meta-detr上获得。

最近提出的检测变压器（DETR）已建立了一个完全端到端的范式以进行对象检测。但是，DETR遭受慢训练的融合，这阻碍了其对各种检测任务的适用性。我们观察到，由于对象查询和编码图像特征之间的语义不一致，DETR的缓慢收敛在很大程度上归因于将对象查询与相关区域匹配的困难。通过此观察，我们设计了与DETR ++（SAM-DETR ++）设计的语义对齐匹配，以加速DETR的收敛并改善检测性能。 SAM-DETR ++的核心是一个插件模块，该模块将对象查询和编码图像功能投射到相同的功能嵌入空间中，在该空间中，每个对象查询都可以轻松地与具有相似语义的相关区域匹配。此外，SAM-DETR ++搜索了多个代表性关键点，并利用其功能以具有增强的表示能力的语义对齐匹配。此外，SAM-DETR ++可以根据设计的语义对准匹配，以粗到5的方式有效地融合多尺度特征。广泛的实验表明，所提出的SAM-DETR ++实现了优越的收敛速度和竞争性检测准确性。此外，作为一种插件方法，SAM-DETR ++可以以更好的性能补充现有的DITR收敛解决方案，仅使用12个训练时代获得44.8％的AP和49.1％的AP，并使用Resnet-50上的CoCo Val2017上的50个训练时代获得50个训练时期。代码可在https://github.com/zhanggongjie/sam-detr上找到。

深层生成模型在逼真的图像合成中取得了显着的进展，并具有多种有条件的输入，而生成多样化但高保真的图像仍然是有条件图像生成的巨大挑战。本文介绍了有条件图像生成的多功能框架，其中包含了CNN的电感偏置和自动回归的强大序列建模，自然会导致图像生成多样化。我们没有像在先前的研究中独立量化多个域的特征，而是设计了一个具有变异正常化程序的集成量化方案，该方案将特征离散化在多个域中，并显着提高了自动回归建模性能。值得注意的是，变异正常器使通过惩罚分布的内域变化来使特征分布在无与伦比的潜在空间中进行正规化。此外，我们设计了一种牙龈样本策略，该策略允许将分配不确定性纳入自动回归训练程序中。牙胶采样大大减轻了暴露偏见，通常会在训练和推理阶段造成未对准并严重损害推理性能。对多条条件图像生成任务进行的广泛实验表明，与最先进的方法相比，我们的方法在定性和定量上实现了卓越的图像生成性能。

培训有效的生成对抗性网络（GANS）需要大量的培训数据，但是训练型模型通常是用鉴别器过度拟合的次优。通过大规模和手工制作的数据增强，通过扩大有限培训数据的分布来解决此问题的几项问题。我们从一个非常不同的角度处理数据限制图像生成。具体而言，我们设计Genco，这是一种生成的共同培训网络，通过引入多种互补鉴别者来减轻鉴别者过度拟合问题，这些判别符号在培训中提供多种独特的观点来提供不同的监督。我们以两种方式实例化了Genco的想法。首先是重量差异共同训练（WECO），其通过多样化它们的参数共同列举多个独特的鉴别器。第二种方式是数据差异共同训练（DACO），其通过馈送具有输入图像的不同视图的鉴别器（例如，输入图像的不同频率分量）来实现共同训练。在多个基准上进行广泛的实验表明，Genco实现了具有有限培训数据的优异发电。此外，Genco还通过组合时补充了增强方法，并在结合时进行了一致和明确的性能。

生成的对抗网络（GAN）在图像翻译和操纵方面取得了巨大成功。但是，具有忠实风格控制的高保真形象生成仍然是计算机视觉中的巨大挑战。本文提出了一种多功能的图像翻译和操纵框架，该框架通过明确构建信件来实现图像生成中准确的语义和样式指导。为了处理通过构建密集的对应关系产生的二次复杂性，我们引入了双层功能对齐策略，该策略采用顶部$ k $操作来对块构成块的功能进行排名，然后在块功能之间进行密集的关注，从而降低了内存成本的降低。由于顶部$ k $操作涉及索引交换，从而排除了梯度传播，因此我们近似具有正则地球搬运工问题的非差异上的顶部$ K $操作，以便可以有效地向后传播其梯度。此外，我们设计了一个新颖的语义位置编码机制，该机制为每个单个语义区域建立坐标，以在建立对应关系时保持纹理结构。此外，我们设计了一种新颖的置信度注入模块，该模块通过根据内置对应关系的可靠性适应特征来减轻不匹配问题。广泛的实验表明，与最先进的方法相比，我们的方法在定性和定量上取得了出色的性能。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.