Industrial image anomaly detection (IAD) is a pivotal topic with huge value. Due to the nature of anomalies, real anomalies in a specific modern industrial domain (i.e., domain-specific anomalies) are usually too rare to collect, which severely hinders IAD. Thus, zero-shot anomaly synthesis (ZSAS), which synthesizes pseudo anomaly images without any domain-specific anomaly, emerges as a vital technique for IAD. However, existing solutions are either unable to synthesize authentic pseudo anomalies, or require cumbersome training. Thus, we focus on ZSAS and propose a brand-new paradigm that can realize both authentic and training-free ZSAS. It is based on a chronically-ignored fact: Although domain-specific anomalies are rare, real anomalies from other domains (i.e., cross-domain anomalies) are actually abundant and directly applicable to ZSAS. Specifically, our new ZSAS paradigm makes three-fold contributions: First, we propose a novel method named Cross-domain Anomaly Injection (CAI), which directly exploits cross-domain anomalies to enable highly authentic ZSAS in a training-free manner. Second, to supply CAI with sufficient cross-domain anomalies, we build the first Domain-agnostic Anomaly Dataset (DAAD) within our best knowledge, which provides ZSAS with abundant real anomaly patterns. Third, we propose a CAI-guided Diffusion Mechanism, which can further break the quantity limit of real anomalies and enable unlimited anomaly synthesis. Our head-to-head comparison with existing ZSAS solutions justifies the superior performance of our paradigm for IAD and demonstrates it as an effective and pragmatic ZSAS solution.

Multi-view clustering (MVC) for remote sensing data has attracted increasing attention due to its ability to exploit complementary information from multiple modalities without requiring labels. Recent graph-based deep clustering methods have shown strong potential in modeling spatial structures inherent in remote sensing data. However, existing approaches often emphasize capturing rich node relations while overlooking the optimization of these relations, leading to noisy connections and weak inter-cluster discrimination. To address this issue, we propose a novel Multi-view Graph Clustering with dual Relation Optimization (MDRO) framework tailored for remote sensing data. Specifically, we first segment the remote sensing image into irregular superpixels to reduce computational complexity and use superpixels as graph nodes. Then, MDRO constructs high-order similarity matrices guided by clustering distribution matrices and performs dual relation optimization to suppress noise relations and strengthen similarity relations. Furthermore, an optimal transportation-based constraint is introduced to guide the formation of robust and balanced cluster assignments, mitigating over-smoothing and trivial solutions in graph learning. Comprehensive experiments on four benchmark remote sensing datasets demonstrate that MDRO consistently outperforms existing single-view and multi-view clustering methods, achieving superior accuracy and robustness.

Deep graph clustering (DGC) for attribute-missing graphs is an unsupervised task aimed at partitioning nodes with incomplete attributes into distinct clusters. Existing imputation methods for attribute-missing graphs often fail to account for the varying amounts of information available across node neighborhoods, leading to unreliable results. To address this issue, we propose a novel method named Divide-Then-Rule Graph Completion (DTRGC). This method first addresses nodes with sufficient known neighborhood information and treats the imputed results as new knowledge to iteratively impute more challenging nodes, while leveraging clustering information to correct imputation errors. Specifically, Dynamic Cluster-Aware Feature Propagation initializes missing node attributes by adjusting propagation weights based on the clustering structure. Subsequently, Hierarchical Neighborhood-Aware Imputation categorizes attribute-missing nodes into three groups based on the completeness of their neighborhood attributes. The imputation is performed hierarchically, prioritizing the groups with nodes that have the most available neighborhood information. The cluster structure is then used to refine the imputation and correct potential errors. Finally, Hop-wise Representation Enhancement integrates information across multiple hops, thereby enriching the expressiveness of node representations. Experimental results on 6 widely used graph datasets show that DTRGC significantly improves the clustering performance of various DGC methods under attribute-missing graphs.

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and AAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods.

In the process of X-ray digital imaging detection, previous test usually adopts the projection data of 360° to reconstruct. But in practical test, it's often restricted to the installation of the equipment and the cost, so it cannot meet the condition of reconstruction. This paper studies a method to solve the coordinate of circle center about a particular component by some experience information, which is based on the projection values of three views in order to determine the distribution of holes. Experiment shows that the method not only reduces the collection angles and the cost, which satisfies the problem of detection in practical application, but also guarantees the accuracy of the positional information of the internal holes, which is compared with the 900 views under reconstruction.

The emergence of technology which contains variable energy data acquisition and intelligent imaging has greatly accelerated the development of image acquisition based on X-ray. Although the emergence of this kind of intelligent technology improves the efficiency of working, the most are accomplished based on the software, and high voltage power supply itself is lack of controlling for the process of high pressure gradient, so that it can't focus on the process of data collection well. Demand on above, we put forward design of the linear gradient of dc high voltage power supply based on X-ray machine, the system of high voltage sccurce starts from hardware and software and solves the previous problem very well at the same time. The high-voltage source uses technology of PID control (Proportional-Integral-Differential Controller), it has the function of the automatic linear gradient of and the slope of process of high pressure gradient is adjustable, and it can get the feedback all the time, the precision and ripple of output are maintained within 0.1%. The development based on the function have been put forward and is in the research, its actual value is relatively significant.