While Tri-plane representation has greatly advanced the development of 3D generative models, problems rooted in its inherent structure, such as multi-face artifacts caused by sharing the same features in symmetric regions, limit its ability to generate complete 360° views. In this paper, we propose CylinderPlane, a novel representation based on the cylindrical coordinate system, to achieve high-quality, artifact-free panoramic image synthesis. Unlike the inevitable feature entanglement in the Cartesian coordinate-based representation, the cylindrical coordinate system explicitly disentangles features at different angles. Consequently, our representation effectively eliminates feature ambiguity and ensures multi-view consistency across full 360°. We further develop a nested cylinder representation that combines cylinder planes of varying radii to achieve multi-scale feature fusion. This design not only addresses the limitations of Tri-plane in modeling complex geometries and varying resolutions, but also mitigates the polar discontinuity inherent in a single cylinder plane. Moreover, our versatile representation can be seamlessly integrated into various generative frameworks and rendering pipelines. Extensive experiments on both synthetic datasets and unstructured in-the-wild images demonstrate that our representation outperforms the existing methods.

Dual-target cross-domain recommendation aims to mitigate data sparsity and enables mutual enhancement via bidirectional knowledge transfer. Most existing methods rely on overlapping users to build cross-domain connections. However, in many real-world scenarios, overlapping data is extremely limited-or even entirely absent-significantly diminishing the effectiveness of these methods. To address this challenge, we propose SUBCDR, a novel framework that leverages large language models (LLMs) to bridge similar users across domains, thereby enhancing dual-target cross-domain recommendation. Specifically, we introduce a Multi-Interests-Aware Prompt Learning mechanism that enables LLMs to generate comprehensive user profiles, disentangling domain-invariant interest points while capturing fine-grained preferences. Then, we construct intra-domain bipartite graphs from user-item interactions and an inter-domain heterogeneous graph that links similar users across domains. Subsequently, to facilitate effective knowledge transfer, we employ Graph Convolutional Networks (GCNs) for intra-domain relationship modeling and design an Inter-domain Hierarchical Attention Network (InterHAN) to facilitate inter-domain knowledge transfer through similar users, learning both shared and specific user representations. Extensive experiments on seven public datasets demonstrate that SUBCDR outperforms state-of-the-art cross-domain recommendation algorithms and single-domain recommendation methods. Our code is publicly available at https://github.com/97z/SUBCDR.git.

Multi-view stereo methods have achieved remarkable progress in recent years, benefiting from advancements in depth and confidence estimation. Existing multi-view stereo methods estimate depth through regression or classification, but both approaches have notable limitations: regression methods are prone to overfitting, while classification methods struggle to achieve precise depth prediction. Combining the strengths of these approaches is crucial for accurate reconstruction. To address these issues, we propose a novel network, termed UML-MVSNet, to enable accurate feature extraction and depth estimation. Specifically, we introduce a Local Transformer (LT) module that applies attention mechanisms to local features, effectively capturing local detail information and enhancing feature matching accuracy. Additionally, we propose an Uncertainty-guided Multi-task Learning (UML) module that integrates the advantages of both regression and classification branches for robust depth estimation. In each sub-branch, the Uncertainty-Guided Optimization (UGO) module is designed to refine the probability volume guided by uncertainty. To guide the network toward low-uncertainty regions and balance multi-task losses, we introduce the Uncertainty-Aware Loss (UA Loss). Extensive experiments on the DTU and Tanks & Temples datasets demonstrate that our UML-MVSNet achieves competitive results in both qualitative and quantitative performance compared to other state-of-the-art methods.

Despite recent advances in dynamic scene reconstruction, challenges from imbalanced camera distribution and inaccurate pose estimation in real-world datasets still persist, undermining the spatiotemporal consistency of reconstruction. In this paper, we propose HybridPlane, a novel representation that leverages the complementary advantages of cylindrical and Cartesian coordinate systems to achieve high-quality dynamic scene synthesis. Unlike Cartesian projection, which shares identical features in symmetric regions, cylindrical projection explicitly disentangles features from different viewpoints, thereby improving robustness against imbalanced camera distributions. Moreover, the synergy between these two coordinate systems in both projection and representational capacity enhances the model's ability to capture complex motions and fine-grained details. We further adopt the dynamic positional encoding strategy to enhance the smoothness of temporal interpolation under inaccurate camera poses by progressively regulating high-frequency signals without incurring additional computational overhead. Extensive experiments demonstrate that our versatile representation can be seamlessly integrated into various rendering pipelines, outperforming the previous methods in reconstruction quality while reducing computational and memory costs by approximately one-third.

Referring Remote Sensing Image Segmentation (RRSIS) is a challenging task that involves segmenting target instances within a top-view image guided by a natural language expression. Existing classic RRSIS methods commonly support target expressions only, i.e., the target described by the expression is present in the image. No-target expressions are excluded. Under this constraint, the model may face significant challenges. For instance, a small error, such as a typographical mistake, could cause a complete failure of the model. To overcome this issue, in this paper, we introduce a new benchmark called Generalized Referring Remote Sensing Image Segmentation (GRRSIS), which extends classic RRSIS by allowing expressions to refer to no-target objects. Towards this, we construct the first large-scale dataset for GRRSIS, called GRRSIS-D, which includes multi-target, single-target, and no-target expressions. Core challenges in GRRSIS stem from the fact that objects in aerial images often occupy only a small number of pixels, exhibit significant orientation variations, and present varying levels of recognition difficulty. To tackle these challenges, we propose an Oriented-aware Multi-Scale Network with an Adaptive Angle Sensing module that integrates Adaptive Rotated Convolution and a gating mechanism to capture diverse object orientations while suppressing irrelevant features for more accurate representations. Additionally, we introduce a novel Online Hard Case Mining Loss, which allocates varying levels of attention to foreground and background regions and reshapes the standard loss by down-weighting well-segmented examples, effectively addressing the issues caused by low pixel occupancy and uneven sample difficulty. The proposed approach achieves state-of-the-art performance on both the newly introduced GRRSIS and classic RRSIS tasks.

The lifespan of a generator greatly depends on the quality and aging of its stator bar insulation material. Aging of insulation materials can lead to premature equipment failure and significant material loss, resulting in substantial economic losses. However, existing methods for predicting the lifespan of electronic wire bars have several drawbacks, such as slow training speed, the need for a large amount of training data, and a tendency to overfit. To address this issue, we propose a characteristic enhancement algorithm based on conditional uncorrelation. This algorithm leverages characteristic enhancement to generate an extensive dataset and utilizes subset selection to identify relevant electrical parameters for predicting the remaining life span of the stator bar’s main insulation configurations. Experimental results demonstrate the advantages of our research compared to deep learning models. Our approach offers a promising solution for accurately predicting the remaining life of stator bar insulation, thereby facilitating effective maintenance planning and minimizing economic losses.

Neural Radiance Fields (NeRF) have shown promise in novel view synthesis, but it still face challenges when applied to refractive objects. The presence of refraction disrupts multiview consistency, often resulting in renderings that are either blurred or distorted. Recent methods alleviate this challenge by introducing external supervision, such as mask images and Index of Refraction. However,acquiring such information is often impractical,limiting the application of NeRF-like models to complex scenes with refracting elementsand yielding unsatisfactory results. To address these limitations, we introduce RoseNeRF (Refracting once is enough for NeRF), a novel method that simplifies the complex interaction of rays within objects to a single refraction event. We design the refraction network that efficiently maps a ray in the 4D light field to its refracted counterpart, better modeling curved ray paths. Furthermore, we introduce a regularization strategy to ensure the reversibility of optical paths, which is anchored in physical world theorems. To help it easier for the network to learn the highly view-dependent appearance of refractive objects, we also propose novel density decoding strategies. Our method is designed for seamless integration into most NeRF-like frameworks and has demonstrated state-of-the-art performance without any additional information on both the Eikonal Fields' dataset and Shiny dataset.

Assembly inspection is a crucial aspect of smart manufacturing to guarantee the quality of products. To address the challenges posed by complex workshop scenes, an assembly inspection algorithm based on multi-view stereo is proposed. Specifically, to enhance the accuracy of reconstruction, the multi-view stereo method utilizes segmentation attention-assisted depth estimation and point cloud fusion to mitigate the noise in the reconstructed point cloud. And the proposed method introduces normalized depth loss to improve the reconstruction ability of foreground pixels. Futhermore, this paper presents a novel 3D point cloud-based size estimation algorithm capable of accurately estimating the size of small objects in complex working scenes. Experimental results demonstrate the efficacy of the improved multi-view stereo algorithm in assembly scenes and verify the superiority of the proposed size estimation algorithm.

Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detection of unseen classes by aligning object region proposals with semantic embeddings. However, these methods have several limitations, including poor region proposals for unseen classes, lack of consideration of semantic representations of unseen classes or their inter-class correlations, and domain bias towards seen classes, which can degrade overall performance. To address these issues, the Trans-ZSD framework is proposed, which is a transformer-based multi-scale contextual detection framework that explicitly exploits inter-class correlations between seen and unseen classes and optimizes feature distribution to learn discriminative features. Trans-ZSD is a single-stage approach that skips proposal generation and performs detection directly, allowing the encoding of long-term dependencies at multiple scales to learn contextual features while requiring fewer inductive biases. Trans-ZSD also introduces a foreground-background separation branch to alleviate the confusion of unseen classes and backgrounds, contrastive learning to learn inter-class uniqueness and reduce misclassification between similar classes, and explicit inter-class commonality learning to facilitate generalization between related classes. Trans-ZSD addresses the domain bias problem in end-to-end generalized zero-shot detection (GZSD) models by using balance loss to maximize response consistency between seen and unseen predictions, ensuring that the model does not bias towards seen classes. The Trans-ZSD framework is evaluated on the PASCAL VOC and MS COCO datasets, demonstrating significant improvements over existing ZSD models.

Anticipating the future behavior of pedestrians is a crucial part of deploying Automated Driving Systems (ADS) in urban traffic scenarios. Most recent works utilize a convolutional neural network (CNN) to extract visual information, which is then input to a recurrent neural network (RNN) along with pedestrian-specific features like location and speed to obtain temporal features. However, the majority of these approaches lack the ability to parse the relationships of the related objects in the specific traffic scene, which leads to omitting the interactions between the pedestrians and the interactions between the pedestrians and the traffic. For this purpose, we propose a graph-structured model which can dig out pedestrians' dynamic constraints by constructing a traffic-aware scene graph within each frame. In addition, to capture pedestrian movement more effectively, we also introduce a temporal feature representation model, which first uses inter-frame and intra-frame GRU (II-GRU) to mine inter-frame information and intra-frame information together, and then employs a novel attention mechanism to adaptively generate attention weights. Extensive experiments on the JAAD and PIE datasets prove that our proposed model is effective in reaching and enhancing the state-of-the-art performance.

Given$m~d$-dimensional responsors and$n~d$-dimensional predictors, sparse regression finds at most$k$predictors for each responsor for linear approximation,$1\leq k \leq d-1$. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. In recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid to the nonapproximate method of subset selection, which is very necessary for many questions in data analysis. Here, we consider sparse regression from the view of correlation and propose the formula of conditional uncorrelation. Then, an efficient nonapproximate method of subset selection is proposed in which we do not need to calculate any coefficients in the regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from$O([{1}/{6}]{k^{3}}\!+(m+1)k^{2}\!+\!mkd)$to$O([{1}/{6}]{k^{3}}\!+[{1}/{2}](m+1)k^{2})$for each candidate subset in sparse regression. Because the dimension$d$is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of nonapproximate subset selection. We also apply the proposed method in real scenarios of dental age assessment and sparse coding to validate the efficiency of the proposed method.

Here we consider linear regression from the view of correlation and propose a robust regression algorithm. The main idea of this work is from the fact that the inliers lying in a low dimensional subspace are mostly correlated, and the presence of outliers leads to the decrease of correlation. We design an iterative outlier removal algorithm based on correlation, by which the outliers can be effectively removed in a normal-distributed or uniform-distributed data set. Finally, the linear equation is calculated based on the remaining points. The experiment results show that the proposed method outperforms the state-of-the-art approaches. In some cases in which outliers are more than inliers, the proposed method can still obtain the real formulas.

Due to the effectiveness in learning the subspace structures, low-rank representation (LRR) and its variations have been widely applied in various fields, such as computer vision and pattern recognition. However, in real applications, it is a challenge to handle the complex noises. To address this problem, we propose a novel robust LRR method based on kernel risk-sensitive loss (KRSL) with high-order manifold constraint, called RHLRR, in which the KRSL is introduced to deal with the noises and the multiple hypergraph regularization term is used as a high order manifold constraint to effectively capture the locality, similarity and the intrinsic geometric information in data. Besides, an iterative algorithm based on the half-quadratic (HQ) and the accelerated block coordinate update (BCU) is developed. The experimental results demonstrate that the proposed method can outperform other state-of-the-art LRR variants.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.

Community question answering (CQA) systems aim to provide users with high-quality answers. Nevertheless, unreliable answers are often returned to users in CQA systems, and the phenomenon causes that users have to browse multiple answers to find the best one. To improve such problem, we design a novel scheme, named PW-CrossCMN. The scheme ranks the candidate answers by pair-wise approach based on numerous historical documents. In the scheme, we apply the preference relationship into deep learning framework. Specifically, the scheme consists of two phases. In phase 1, the scheme extracts the features via automated feature engineering to construct the preference vectors and then divides the vectors into balanced positive and negative training samples based on the preference relationship. In phase 2, we build the CrossCMN model, which implements the multi-network parallel convolution and the cross forward propagation of full-connected layers, to achieve training and prediction tasks. Moreover, the multi-layer perception (MLP) is introduced to extract combination features in the prediction module. We perform extensive experiments on two typical datasets, and the results show that our scheme has more excellent performance in answer ranking task compared with several state-of-the-art baselines. In addition, we have released the relevant codes.

Semantic segmentation is a challenging computer visual task which needs enormous pixel-level annotation data. But collecting a large amount of pixel-level annotation data is labor intensive. To address this issue, our work focuses on weakly-supervised learning approach which combines the adversarial learning and localization ability of classification model together, in this way, data with different annotations can be fully utilized. Specifically, the adversarial learning encourages the high order spatial consistences thus offers a relatively reliable initial confidence map. And we find that the hybrid atrous rate (HAR) can improve the localization ability of the classification model, thus indicate more precise object-related regions, which serves as strong supervision information. We conduct experiments with different settings to demonstrate the effectiveness of this weakly-supervised learning approach. The results show that our approach can improve the performance of baseline adversarial learning from 73.2 to 75.1 (mIOU), which is pretty effective.

Modeling the relationship among human joints is one of the most important components in human pose estimation. Most of previous methods define this relationship as a geometric constraint on the relative locations of two neighboring joints. In this constraint, the local appearance of the region connecting two neighboring joints is ignored. However, discarding this image appearance leads to some severe problems, such as double-counting and localization failure when the human pose is rare in the training dataset. Moreover, this image appearance, called human limb, plays an important role in human pose estimation in human visual system. Due to these reasons, we propose to solve a new task: human limb detection, which aims at detecting and representing this local image appearance. We combine this task with human joint localization as a unified framework. After getting the initial detections, we design a two-steps graphical model to capture the spatial relationship among human joints and limbs in a coarse to fine way. We evaluate the proposed method on two widely used datasets for human pose estimation: 1) frame labeled in cinema and 2) leeds sports pose datasets. The experiments results show the effectiveness of our method.

Kernel least mean square is a simple and effective adaptive algorithm, but dragged by its unlimited growing network size. Many schemes have been proposed to reduce the network size, but few takes the distribution of the input data into account. Input data distribution is generally important in view of both model sparsification and generalization performance promotion. In this paper, we introduce an online density-dependent vector quantization scheme, which adopts a shrinkage threshold to adapt its output to the input data distribution. This scheme is then incorporated into the quantized kernel least mean square (QKLMS) to develop a density-dependent QKLMS (DQKLMS). Experiments on static function estimation and short-term chaotic time series prediction are presented to demonstrate the desirable performance of DQKLMS.

The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.

Fractal image compression (FIC) is an image coding technology based on the local similarity of image structure. It is widely used in many fields such as image retrieval, image denoising, image authentication, and encryption. FIC, however, suffers from the high computational complexity in encoding. Although many schemes are published to speed up encoding, they do not easily satisfy the encoding time or the reconstructed image quality requirements. In this paper, a new FIC scheme is proposed based on the fact that the affine similarity between two blocks in FIC is equivalent to the absolute value of Pearson's correlation coefficient (APCC) between them. First, all blocks in the range and domain pools are chosen and classified using an APCC-based block classification method to increase the matching probability. Second, by sorting the domain blocks with respect to APCCs between these domain blocks and a preset block in each class, the matching domain block for a range block can be searched in the selected domain set in which these APCCs are closer to APCC between the range block and the preset block. Experimental results show that the proposed scheme can significantly speed up the encoding process in FIC while preserving the reconstructed image quality well.

Since Jacquin proposed original fractal image compression technique in 1990, fractal coding method has been developed into various schemes. Traditionally, fractal coding uses mean square error (MSE) to evaluate similarity of image blocks, but the similarity evaluated by MSE usually differs from human visual system (HVS). Compared with MSE, structural similarity (SSIM) is an image measure index which is more appropriate for the HVS. This paper proposes a new fractal coding scheme which uses structural similarity to measure the similarity between image blocks and compute these blocks' coefficients. The experiment results show that the proposed method generates higher quality images for the HVS than MSE scheme.