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Arbitrary style transfer (AST) has garnered considerable attention for its ability to transfer styles infinitely. Although existing methods have achieved impressive results, they may overlook style consistencies and fail to capture crucial style patterns, leading to inconsistent style transfer (ST) caused by minor disturbances. To tackle this issue, we conduct a mathematical analysis of inconsistent ST and develop a style inconsistency measure (SIM) to quantify the inconsistencies between generated images. Moreover, we propose a consistent AST (CAST) framework that effectively captures and transfers essential style features into content images. The proposed CAST framework incorporates an intersection-of-union-preserving crop (IoUPC) module to obtain style pairs with minor disturbance, a self-attention (SA) module to learn the crucial style features, and a style inconsistency loss regularization (SILR) to facilitate consistent feature learning for consistent stylization. Our proposed framework not only provides an optimal solution for consistent ST but also outperforms existing methods when embedded into the CAST framework. Extensive experiments demonstrate that the proposed CAST framework can effectively transfer style patterns while preserving consistency and achieve the state-of-the-art performance.

Multiview clustering (MVC), which can dexterously uncover the underlying intrinsic clustering structures of the data, has been particularly attractive in recent years. However, previous methods are designed for either complete or incomplete multiview only, without a unified framework that handles both tasks simultaneously. To address this issue, we propose a unified framework to efficiently tackle both tasks in approximately linear complexity, which integrates tensor learning to explore the inter-view low-rankness and dynamic anchor learning to explore the intra-view low-rankness for scalable clustering (TDASC). Specifically, TDASC efficiently learns smaller view-specific graphs by anchor learning, which not only explores the diversity embedded in multiview data, but also yields approximately linear complexity. Meanwhile, unlike most current approaches that only focus on pair-wise relationships, the proposed TDASC incorporates multiple graphs into an inter-view low-rank tensor, which elegantly models the high-order correlations across views and further guides the anchor learning. Extensive experiments on both complete and incomplete multiview datasets clearly demonstrate the effectiveness and efficiency of TDASC compared with several state-of-the-art techniques.

Due to the high cost of pixel-level labels required for fully-supervised semantic segmentation, weakly-supervised segmentation has emerged as a more viable option recently. Existing weakly-supervised methods tried to generate pseudo-labels without pixel-level labels for semantic segmentation, but a common problem is that the generated pseudo-labels contain insufficient semantic information, resulting in poor accuracy. To address this challenge, a novel method is proposed, which generates class activation/attention maps (CAMs) containing sufficient semantic information as pseudo-labels for the semantic segmentation training without pixel-level labels. In this method, the attention-transfer module is designed to preserve salient regions on CAMs while avoiding the suppression of inconspicuous regions of the targets, which results in the generation of pseudo-labels with sufficient semantic information. A pixel relevance focused-unfocused module has also been developed for better integrating contextual information, with both attention mechanisms employed to extract focused relevant pixels and multi-scale atrous convolution employed to expand receptive field for establishing distant pixel connections. The proposed method has been experimentally demonstrated to achieve competitive performance in weakly-supervised segmentation, and even outperforms many saliency-joined methods.

For autonomous ground vehicles, global localization with 3D LiDAR is an indispensable part of tasks such as navigation. Usually, global localization using LiDAR is subdivided into two sub-problems, place recognition and global registration. For place recognition, the recent emerging schemes based on deep learning either rely on 3D convolution with high complexity or need to learn features from various forward perspectives. To mitigate this, we propose a model with roll-pitch-yaw invariance that represents point clouds as probabilistic voxels and generates occupancy grids from a bird’s-eye view, fulfilling robust place recognition by learning aggregated embeddings from a fixed perspective. For low-overlap global registration, the traditional handcraft feature-based methods are mostly limited to dense object-level point clouds, while the state-of-the-art learning-based approaches often rely on complex 3D convolution and additional feature association learning. To fill this gap to some extent, we propose to estimate the relative roll-pitch angles and vertical translation by fitting and aligning the ground plane of the point clouds and to determine the horizontal translations and yaw angle by matching their projected occupancy grids. Extensive experiments corroborate the superior recall and generalization ability of our place recognition model, as well as the advanced success rate and accuracy of our 3D registration approach. Especially in the recognition and registration of hard samples, our results far exceed those of our counterparts by large margins. To ensure full reproducibility, the relevant codes and data are made available online at https://cslinzhang.github.io/GLoc/GLoc.html.

The harmonic neural network (HNN) learns a combination of discrete cosine transform (DCT) filters to obtain an integrated feature from all spectra in the frequency domain. HNN, however, faces two challenges in learning and inference processes. First, the spectrum feature learned by HNN is insufficient and limited because the number of DCT filters is much smaller than that of feature maps. In addition, the number of parameters and the computation costs of HNN are significantly high because the intermediate spectrum layers are expanded multiple times. These two challenges will severely harm the performance and efficiency of HNN. To solve these problems, we first propose the compound DCT (C-DCT) filters integrating the nearest DCT filters to retrieve rich spectrum features to improve the performance. To significantly reduce the model size and computation complexity for improving the efficiency, the shared reconstruction filter is then proposed to share and dynamically drop the meta-filters in every frequency branch. Integrating the C-DCT filters with the shared reconstruction filters, the efficient harmonic network (EH-Net) is introduced. Extensive experiments on different datasets demonstrate that the proposed EH-Nets can effectively reduce the model size and computation complexity while maintaining the model performance. The code has been released at https://github.com/zhangle408/EH-Nets.

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video content and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this article, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and interframe inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces from a global perspective, respectively. The two components are designed using a novel long-distance attention mechanism. One component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves state-of-the-art performance, and the proposed long-distance attention method can effectively capture pivotal parts for face forgery.

Variable selection methods aim to select the key covariates related to the response variable for learning problems with high-dimensional data. Typical methods of variable selection are formulated in terms of sparse mean regression with a parametric hypothesis class, such as linear functions or additive functions. Despite rapid progress, the existing methods depend heavily on the chosen parametric function class and are incapable of handling variable selection for problems where the data noise is heavy-tailed or skewed. To circumvent these drawbacks, we propose sparse gradient learning with the mode-induced loss (SGLML) for robust model-free (MF) variable selection. The theoretical analysis is established for SGLML on the upper bound of excess risk and the consistency of variable selection, which guarantees its ability for gradient estimation from the lens of gradient risk and informative variable identification under mild conditions. Experimental analysis on the simulated and real data demonstrates the competitive performance of our method over the previous gradient learning (GL) methods.

Owing to the inherent complementarity among LiDAR, camera, and IMU, a growing effort has been paid to laser-visual-inertial SLAM recently. The existing approaches, however, are limited in two aspects. First, at the front-end, they usually employ a discrete-time representation that requires high-precision hardware/software synchronization and are based on geometric laser features, leading to low robustness and scalability. Second, at the backend, visual loop constraints suffer from scale ambiguity and the sparseness of the point cloud deteriorates the scan-to-scan loop detection. To solve these problems, for the front-end, we propose a continuous-time laser-visual-inertial odometry which formulates the carrier trajectory in continuous time, organizes point clouds in probabilistic submaps, and jointly optimizes the loss terms of laser anchors, visual reprojections, and IMU readings, achieving accurate pose estimation even with fast motion or in unstructured scenes where it is difficult to extract meaningful geometric features. At the backend, we propose building 5-DoF laser constraints by matching projected 2D submaps and 6-DoF visual constraints via laser-aided visual relocalization, ensuring mapping consistency in large-scale scenes. Results show that our framework achieves high-precision estimation and is more robust than its counterparts when the carrier works in large scenes or with fast motion. The relevant codes and data are open-sourced at https://cslinzhang.github.io/Ct-LVI/Ct-LVI.html.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an $L_{1}$ norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the $L_{1}$ norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.

Vehicular Ad-hoc Networks (VANETs) provide key support for the achievement of intelligent, safe, and efficient driverless transportation systems through real-time communication between vehicles and vehicles, and vehicles and road infrastructure. This paper investigates a joint optimization problem of electric vehicles (EVs) charging management and resource allocation based on VANETs. EV charging requires significantly more time than refueling conventional vehicles, a key factor behind people's reluctance to transition from internal combustion engine vehicles to EVs. Previous works have primarily concentrated on fully-charged vehicles and random matching, which does not solve the problems of vehicle charging delays and long customer waiting times. Considering these factors, we proposes a distributed multi-level charging strategy and level-by-level matching method. Specifically, EVs and passengers are categorized into classes based on battery power and target mileage. Vehicles are then allocated to customers in the same or lower levels. Furthermore, the Attentive Temporal Convolutional Networks-Long Short Term Memory (ATCN-LSTM) model is leveraged to predict historical traffic data, supporting anticipatory decision-making. Subsequently, we develop a hierarchical charging and rebalancing joint optimization framework that incorporates charging facility planning. Experimental results obtained under various model parameters exhibit the method's commendable performance, as evidenced by metrics such as operating cost, system response time, and vehicle utilization.