Anomaly detection is challenging with the presence of missing data. The common two-stage strategy first imputes missing values and then performs anomaly detection, making its performance highly dependent on imputation quality. When the data distribution is complex, the imputation quality may be poor, which degrades the performance of anomaly detectors. To address this issue, we propose a Missing-Kernel-based One-Class Support Vector Machine (MK-OCSVM), in which a novel missing kernel capable of handling missing data is constructed. MK-OCSVM avoids explicit imputation so that its anomaly detection performance is not affected by the imputation quality. Besides, we use the partially observed data in the observation space to model the data distribution in the latent original space, thereby enabling anomaly detection for the latent complete data. MK-OCSVM incorporates the data distribution modeling and OCSVM construction in a joint optimization framework, which alleviates the singularity issue and enhances the suitability of the missing kernel for anomaly detection. To solve the joint optimization problem of MK-OCSVM, a gradient-based alternating optimization algorithm is designed. Experimental results demonstrate the effectiveness of the proposed method.

Subject-driven image personalization has seen notable advancements, especially with the ReferenceNet paradigm, which excels in integrating reference image features for creative and commercial applications. However, current ReferenceNet implementations mainly function as latent-level feature extractors, limiting their potential. This restricts the delivery of suitable features to the denoising backbone across timesteps, resulting in suboptimal image consistency. In this paper, we revisit reference feature extraction and propose TFCustom, a framework that focuses on reference image features at different temporal and frequency levels. We introduce synchronized ReferenceNet to extract reference features while optimizing noise injection and denoising. We also propose a time-aware frequency refinement module that uses high- and low-frequency filters with time embeddings to adaptively select reference feature injection. Additionally, we introduce a reward-based loss to improve the similarity between reference objects and generated images. Experimental results show that TFCustom outperforms existing methods in single-object and multi-object reference generation, with significant improvements in textual details.

Anomaly detection plays an important role in industry, especially in ensuring system safety and product quality. Due to the unavailability of anomalous data in many practical cases, anomaly detection is usually solved by one-class classification (OCC) methods using only normal data. As a classical OCC method, one-class support vector machine (OCSVM) is a popular discriminative approach for anomaly detection, which detects abnormal data points by establishing a decision boundary in the kernel space. However, the performance of OCSVM heavily relies on kernel parameters, whose selection is not trivial for anomaly detection problems. Moreover, for some uneven and complex data distributions, different data regions may have quite different densities and shapes, making it difficult for OCSVM to obtain good boundaries in all regions using a global kernel parameter. To address the above two issues, in this article, we propose a hybrid algorithm incorporating vector quantization and OCSVM (VQ-OCSVM). Specifically, vector quantization is used to extract distribution information of normal data, and the results are used to construct an explicit mapping function to map data into a high-dimensional feature space. Then, OCSVM is performed in the feature space to build a classifier. By introducing the explicit mapping into OCSVM, the proposed method can effectively bypass the kernel parameter selection problem of the classical OCSVM method. Furthermore, the constructed mapping carries the data distribution information, and the VQ-OCSVM model can be regarded as an integration of generative learning and discriminative learning. The complementary properties of these two paradigms make the proposed VQ-OCSVM algorithm have better generalization capacity for complex data distribution. Both qualitative and quantitative experimental results demonstrate the effectiveness and advantages of the proposed method.