3D场景流动表征了当前时间的点如何流到3D欧几里得空间中的下一次，该空间具有自主推断场景中所有对象的非刚性运动的能力。从图像估算场景流的先前方法具有局限性，该方法通过分别估计光流和差异来划分3D场景流的整体性质。学习3D场景从点云流动也面临着综合数据和真实数据与LIDAR点云的稀疏性之间差距的困难。在本文中，利用生成的密集深度图来获得显式的3D坐标，该坐标可直接从2D图像中学习3D场景流。通过将2D像素的密度性质引入3D空间，可以改善预测场景流的稳定性。通过统计方法消除了生成的3D点云中的离群值，以削弱噪声点对3D场景流估计任务的影响。提出了差异一致性损失，以实现3D场景流的更有效的无监督学习。比较了现实世界图像上3D场景流的自我监督学习方法与在综合数据集中学习的多种方法和在LIDAR点云上学习的方法。显示多个场景流量指标的比较可以证明引入伪LIDAR点云到场景流量估计的有效性和优势。

有希望的互补性存在着颜色图像的纹理特征和激光点云的几何信息。但是，在3D对象检测领域中，仍然存在许多挑战，以实现高效且可靠的特征融合。在本文中，首先，在2D平面中填充了非结构化的3D点云，并且使用投影感知的卷积层更快地提取3D点云特征。此外，在数据预处理中提前建立了不同传感器信号之间的相应索引，从而实现更快的交叉模式融合。为了解决LIDAR点和图像像素的未对准问题，提出了两个新的插件融合模块，即licamfuse和bilicamfuse。在Licamfuse中，提出了带有双峰特征的欧几里得距离的软查询权重。在Bilicamfuse中，提出了双重注意的融合模块，以深层关联场景的几何和纹理特征。 KITTI数据集上的定量结果表明，所提出的方法可以实现更好的特征级融合。此外，与现有方法相比，建议的网络显示出更短的运行时间。

在现有方法中，LIDAR的探测器显示出卓越的性能，但视觉探测器仍被广泛用于其价格优势。从惯例上讲，视觉检验的任务主要依赖于连续图像的输入。但是，探测器网络学习图像提供的异性几何信息非常复杂。在本文中，将伪LIDAR的概念引入了探测器中以解决此问题。伪LIDAR点云背面项目由图像生成的深度图中的3D点云，这改变了图像表示的方式。与立体声图像相比，立体声匹配网络生成的伪lidar点云可以得到显式的3D坐标。由于在3D空间中发生了6个自由度（DOF）姿势转换，因此伪宽点云提供的3D结构信息比图像更直接。与稀疏的激光雷达相比，伪驱动器具有较密集的点云。为了充分利用伪LIDAR提供的丰富点云信息，采用了投射感知的探测管道。以前的大多数基于激光雷达的算法从点云中采样了8192点，作为探视网络的输入。投影感知的密集探测管道采用从图像产生的所有伪lidar点云，除了误差点作为网络的输入。在图像中充分利用3D几何信息时，图像中的语义信息也用于探视任务中。 2D-3D的融合是在仅基于图像的进程中实现的。 Kitti数据集的实验证明了我们方法的有效性。据我们所知，这是使用伪LIDAR的第一种视觉探光法。

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade the performance. To address this issue, we propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions, which tends to be minimally affected by the bias caused by the outliers. In particular, we formalize the robust consensus clustering problem as a constraint optimization problem, and then derive an effective algorithm upon alternating direction method of multipliers (ADMM) with rigorous convergence guarantee. Our method outperforms the baselines on benchmarks. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks using the proposed consensus clustering results based on the similarity computed via Kolmogorov-Smirnov Statistics. The accurate clustering result is helpful for building the advertiser profiles so as to perform the forecasting.

Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL

We study algorithms for detecting and including glass objects in an optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in this work. When LiDAR data is the primary exteroceptive sensory input, glass objects are not correctly registered. This occurs as the incident light primarily passes through the glass objects or reflects away from the source, resulting in inaccurate range measurements for glass surfaces. Consequently, the localization and mapping performance is impacted, thereby rendering navigation in such environments unreliable. Optimization-based SLAM solutions, which are also referred to as Graph SLAM, are widely regarded as state of the art. In this paper, we utilize a simple and computationally inexpensive glass detection scheme for detecting glass objects and present the methodology to incorporate the identified objects into the occupancy grid maintained by such an algorithm (Google Cartographer). We develop both local (submap level) and global algorithms for achieving the objective mentioned above and compare the maps produced by our method with those produced by an existing algorithm that utilizes particle filter based SLAM.

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.

Image-based head swapping task aims to stitch a source head to another source body flawlessly. This seldom-studied task faces two major challenges: 1) Preserving the head and body from various sources while generating a seamless transition region. 2) No paired head swapping dataset and benchmark so far. In this paper, we propose an image-based head swapping framework (HS-Diffusion) which consists of a semantic-guided latent diffusion model (SG-LDM) and a semantic layout generator. We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping. SG-LDM can further implement fine-grained head swapping with the blended layout as condition by a progressive fusion process, while preserving source head and source body with high-quality reconstruction. To this end, we design a head-cover augmentation strategy for training and a neck alignment trick for geometric realism. Importantly, we construct a new image-based head swapping benchmark and propose two tailor-designed metrics (Mask-FID and Focal-FID). Extensive experiments demonstrate the superiority of our framework. The code will be available: https://github.com/qinghew/HS-Diffusion.

Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. Evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 7 evolving registration approaches and 4 traditional registration approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.