Feature correspondence, particularly distinguishing inliers (true matches) from outliers (false matches), remains a core challenge in geometric computer vision. We present HAT-Match, a novel Graph Transformer framework with Hybrid Attention for two-view correspondence pruning. HAT-Match integrates three types of attention mechanismsself-attention for modeling pairwise dependencies, SE-based channel attention for emphasizing salient feature channels, and global global structure-aware for capturing structure-aware consistency. To model local geometric relationships, we first generate coarse clusters using a permutation-equivariant graph pooling and unpooling mechanism, which serves as an initial grouping of potentially consistent correspondences. Based on the resulting embeddings, we further construct local graphs using a DGCNN-style k-nearest neighbor (KNN) strategy, enabling the modeling of fine-grained local dependencies. These local graphs are then passed through a hybrid attention module that jointly encodes local and global contextual features. To refine correspondence confidence, we apply graph Laplacian-based attention over the global graph, enhancing discriminative feature propagation. The entire architecture is integrated into a progressive pruning framework that iteratively removes outliers and updates correspondence weights. Extensive experiments demonstrate that HAT-Match achieves state-of-the-art results across various challenging tasks, including relative pose estimation and visual localization, on both indoor and outdoor datasets.

Correspondence learning is a crucial component in multiview geometry and computer vision. The presence of heavy outliers (mismatches) consistently renders the matching problem to be highly challenging. In this article, we revisit the benefits of local consensus (LC) in traditional feature matching and introduce the concept of LC to design a trainable neural network capable of capturing the underlying correspondences. This network is named the LC transformer (LCT) and is specifically tailored for wide-baseline stereo applications. Our network architecture comprises three distinct operations. To establish the neighbor topology, we employ a dynamic graph-based embedding layer as the initial step. Subsequently, these local topologies serve as guidance for the multihead self-attention layer, enabling it to extract a more extensive contextual understanding through channel attention (CA). Following this, order-aware graph pooling is applied to extract the global context information from the embedded LC. Through the experimental analysis, the ablation study reveals that PointNet-like learning models can, indeed, benefit from the incorporation of LC. The proposed model achieves state-of-the-art performance in both challenging scenes, namely, the YFCC100M outdoor and SUN3D indoor environments, even in the presence of more than 90% outliers.

The increasing prevalence of smart services in daily life drives the rapid emergence of service ecosystems across various domains such as E-commerce, cloud manufacturing, and crowdsourcing. Evaluating the utility of these ecosystems is challenging due to complex characteristics like diverse crowd intelligence, cascading service network effects, and the interplay of individual interests. To address these challenges, this study introduces an innovative utility evaluation model that integrates individual and systemic factors with multidimensional metrics, reconciling micro and macro perspectives. This model effectively addresses potential conflicts between individual and systemic benefits, supporting the continuous learning and evolution of agents within service ecosystems. It employs value entropy to precisely model and interpret complex nonlinear emergence phenomena. The model's universal framework offers high customizability and broad applicability, facilitating specific adaptations across different service ecosystem scenarios. Additionally, a visual multi-agent system simulation tool has been developed to adjust agent attributes and cooperative topologies, allowing for the observation of system responses. Empirical validation confirms the model's accuracy and provides a mechanistic explanation for emergence phenomena in human societies. The findings demonstrate the model's high coordination and effectiveness in managing both linear and nonlinear characteristics, presenting a powerful and flexible tool for utility evaluation in service ecosystems. Our code is available at https://github.com/yxn9191/value_entropy.

The main problems in point registration involve recovering correspondences and estimating transformations, especially in a fully unsupervised way without any feature descriptors. In this work, we propose a robust point matching method using discrete optimal transport (OT), which is a natural and useful approach for assignment tasks, to recover the underlying correspondences and improve the nonrigid registration in the presence of unknown global transformations. Specifically, we cast the registration problem as a joint estimation over local transport couplings and global transformations, observing that the local neighborhood topology structures should be preserved strongly and stably for nonrigid transformations. By solving the Gromov-Wasserstein discrepancy, a smooth assignment matrix from one point set to another can be recovered in a fully unsupervised way. Registration performance can be improved by applying an unsupervised map to guide the transformation estimate under the alternating optimization. Experimental results on several datasets reveal how the presented method is superior to the state-of-the-art methods when facing large data degradations.

Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.

Correspondence learning is a vital component in multi-view geometry and computer vision. The heavy outliers make the matching problem very challenging. By revisiting the local consensus benefits of traditional feature matching, we introduce the local consensus to design a learnerable neural network to capture underlying correspondences, dubbed the Local Consensus Transformer, for wide-baseline stereo. Specifically, our network architecture consists of three operations. In order to construct the neighbor topology, a dynamic graph-based embedding layer is used first. These local topologies then guide the multihead self-attention layer to mine a greater amount of context through channel attention. After that, order-aware graph pooling is applied to extract global context from the embedded local consensus. Experimentally, the abalation study shows that pointnet-like learning models can benefit from local consensus. The proposed model achieves state-of-the-art performance on both the YFCC100M outdoor and SUN3D indoor challenging scenes with more than 90 percent outliers.

Unsupervised Image-to-Image generation has obtained many studies recently, where generative adversarial networks (GANs) have developed as an effective model. Cycle consistency or dual learning guides the GAN from aligned image pairs to the unpaired training set and can be applied for many applications based on the image-to-image generation. However, for many tasks, error accumulation, which can be produced in the progress of image reconstruction, affects the realism and quality of the generated images. To eliminate error accumulation and guide the adversarial learning, in this paper, we propose dual-cycle consistent GAN (DucGAN), a novel approach for cross-domain image-to-image generation. The key idea is to introduce dual-cycle learning, which restrains error accumulation. In our method, inter-cycle and extra-cycle are based on the cycle consistency, while the output from intercycle is cast as a new augmented input in extra-cycle. On the one hand, reconstruction loss in extra-cycle can constrain and guide the training. On the other hand, dual-cycle learning is self-augmented. Our extensive experiments on image-to-image generation tasks show that DucGAN is effective and superior to several state-of-the-art GANs.

While point set registration has been studied in many areas of computer vision for decades, registering points encountering different degradations remains a challenging problem. In this article, we introduce a robust point pattern matching method, termed spatially coherent matching (SCM). The SCM algorithm consists of recovering correspondences and learning nonrigid transformations between the given model and scene point sets while preserving the local neighborhood structure. Precisely, the proposed SCM starts with the initial matches that are contaminated by degradations (e.g., deformation, noise, occlusion, rotation, multiview, and outliers), and the main task is to recover the underlying correspondences and learn the nonrigid transformation alternately. Based on unsupervised manifold learning, the challenging problem of point set registration can be formulated by the Gaussian fields criterion under a local preserving constraint, where the neighborhood structure could be preserved in each transforming. Moreover, the nonrigid transformation is modeled in a reproducing kernel Hilbert space, and we use a kernel approximation strategy to boost efficiency. Experimental results demonstrate that the proposed approach robustly rejecting mismatches and registers complex point set pairs containing large degradations.

In order to solve the registration problem, we propose a robust method called Spatially Coherent Matching (SCM), where it can get the underlying correspondences from the given putative sets of feature points for robust matching, and estimate the transformation for robust registration. Recovering correct matches and fitting transformations between image pairs are key components in the field of pattern recognition. The proposed SCM starts with a putative correspondence set which is contaminated by degradations (e.g., occlusion, deformation, rotation, and outliers), and the main goal is to identify the true correspondences and estimate the underlying transformation. Then we formulate this challenging problem by the spatially coherent matching model with a robust exponential distance loss and a spatial constraint. Based on the regularization theory, SCM preserves the topological structure of the adjacent features. Moreover, a sparse approximation strategy is used to improve the efficiency. Finally, the experimental results reveal that the proposed method outperforms current state-of-the-art methods in most test scenarios on several real image datasets and synthesized datasets.

Multimodal retinal image registration plays an important role in medical image analysis. In this field, retinal images from different modalities are aligned together to achieve a more evaluable fusion image for diagnoses. One of the challenging problem solved in this paper is the low success rate in multimodal retinal image registration. An automated point set registration framework is proposed to solve the problem. The framework includes three parts: feature point extraction and robust initial point matching, matching postprocessing, adaptive mismatches removing and transformation estimation. The experimental results show that our proposed framework is robust to outliers and repeated pattern and it obtains a more stable and accurate result than state-of-the-art methods.

Estimating transformations from degraded point sets is necessary for many computer vision and pattern recognition applications. In this paper, we propose a robust non-rigid point set registration method based on spatially constrained context-aware Gaussian fields. We first construct a context-aware representation (e.g., shape context) for assignment initialization. Then, we use a graph Laplacian regularized Gaussian fields to estimate the underlying transformation from the likely correspondences. On the one hand, the intrinsic manifold is considered and used to preserve the geometrical structure, and a priori knowledge of the point set is extracted. On the other hand, by using the deterministic annealing, the presented method is extended to a projected high-dimensional feature space, i.e., reproducing kernel Hilbert space through a kernel trick to solve the transformation, in which the local structure is propagated by the coarse-to-fine scaling strategy. In this way, the proposed method gradually recovers much more correct correspondences, and then estimates the transformation parameters accurately and robustly when facing degradations. Experimental results on 2D and 3D synthetic and real data (point sets) demonstrate that the proposed method reaches better performance than the state-of-the-art algorithms.

Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications. Although widely used, existing PSR methods cannot align point sets robustly under degradations, such as deformation, noise, occlusion, outlier, rotation, and multi-view changes. This paper proposes context-aware Gaussian fields (CA-LapGF) for nonrigid PSR subject to global rigid and local non-rigid geometric constraints, where a laplacian regularized term is added to preserve the intrinsic geometry of the transformed set. CA-LapGF uses a robust objective function and the quasi-Newton algorithm to estimate the likely correspondences, and the non-rigid transformation parameters between two point sets iteratively. The CA-LapGF can estimate non-rigid transformations, which are mapped to reproducing kernel Hilbert spaces, accurately and robustly in the presence of degradations. Experimental results on synthetic and real images reveal that how CA-LapGF outperforms state-of-the-art algorithms for non-rigid PSR.

Point set registration problem is challenging to solve in the presence of outliers. In this paper, we proposed a registration method based on fuzzy correspondences and kernel density estimation. The main idea of our method is that the moving point set consists of inliers represented using a mixture of Gaussian, and outliers represented via an additional uniform distribution, then we use the fuzzy correspondences to estimate the Gaussian elements in the mixture model. There are four parts of the paper: we formulate the contaminated point set registration problem as a mixture model according to the well known Gaussian mixture model (GMM) based method firstly. Secondly, Gaussian elements are estimated by fuzzy correspondences to increase the registration accuracy efficiently. Thirdly, the optimal transformation between two contaminated point sets is expressed by representation theorem, and solved by EM algorithm iteratively. Finally, we compare our proposed method with several state-of-the-art methods, and the results show that our method gets better performances than the other methods in most tested scenarios.