The widespread application of time-series data has raised higher demands for the accuracy and robustness of anomaly detection. Traditional anomaly detection methods rely on space-frequency perception techniques, expanding the receptive field through frequency-domain transformations. However, the feature confusion between amplitude and phase spectra limits detection performance. To address this issue, we propose a time-series anomaly detection method based on frequency-domain decoupling and correction. The method incorporates two core designs: amplitude-phase decoupling and spectral centroid correction. By separating the complex space in the frequency domain, we independently model amplitude and phase information to avoid feature confusion. Subsequently, the spectral centroid is introduced as a basis for frequency-domain correction to measure the energy distribution of frequency components, adjusting the decoupled amplitude and phase components. This enables the spectrum to more accurately reflect signal characteristics and reduces noise interference. The proposed method provides a finer-grained frequency-domain perspective, enhances the perception of frequency-domain information, and significantly improves the accuracy and robustness of anomaly detection.

Unsupervised domain adaptation (UDA) methods have recently been explored for their use in skeleton recognition tasks. Much work along this line has been focusing on the “close-set” problems, which often deviate from reality as human actions vary in application scenarios. Thus, there remains a need to thoroughly study the “open-set” problems with UDA methods for skeleton recognition, aiming to support those models capable of self-adapting to action changes in different scenarios. To this end, we delve into the “open-set” problems from a feature alignment perspective under UDA settings in reaching domain and class alignment. Specifically, the domain-wise alignment was achieved by the maximum mean discrepancy (MMD) combined with supervision signals from the source domain, which form clear feature boundaries between the “known” and “unknown” classes. Then, the class-wise alignment was achieved by contrastive learning methods, which are distinguished from previous binary classification methods, in reaching compactness inside of “unknown” or “known” classes. Moreover, we conducted the Fourier analysis during the evaluation phases to verify the model’s robustness. To our knowledge, we are the first to apply the Fourier Heatmap in UDA methods for skeleton recognition. The heatmap visualizes the model’s sensitivity steered for interpretability. Significant performance improvements are observed on the NTU and PKU data sets when adding the domain-wise alignment module to other contrastive learning methods. Furthermore, experimental results demonstrate that our approach, termed CStrCRL-UDA, is consistent with robustness and efficiency on these two benchmark data sets.

Contrastive learning has been widely embraced for its notable success along with two augmentation methods— normal and strong augmentations—in skeleton action recognition. Existing methods gain performance largely by customizing normal augmentations while bypassing strong augmentations that riches in motion patterns. To make up for the blank, we propose a novel framework, called CStrCRL, acquiring view-invariant and discriminative features from strong augmentations by leveraging contrastive learning. Specifically, to avoid the fragility of skeleton data adversely affecting the model after applying strong augmentations, we use consistency learning to maximize the similarity between strongly and normally augmented views. Furthermore, we employ cross-view learning on strong and normal augmentations for eliminating uncertainty feature boundaries learned by the model. Moreover, we design a new backbone, termed GatedStrNet, for discriminating valid and invalid features contained in strong augmented views. Finally, extensive experiments on NTU 60/120 and PKUMMD II demonstrate that the proposed method bridges the performance gap between normal and strong augmentations on contrastive learning of skeleton recognition. Notably, with a single stream input, CStrCRL achieves accuracies of 78.93% and 84.04% on the NTU60 Xsub and Xview datasets. Our source code can be found at: https://github.com/RHu-main/CStrCRL.

With the development of computer vision, the semantic segmentation of remote sensing images, which has become an important topic, has been utilized in various applications for image content analysis and understanding, such as urban planning, natural disaster monitoring, and land resource management. Many approaches have been proposed to address these problems. However, due to obvious differences in resolution, spatial structure, and semantics between remote sensing images and ordinary images, the semantic segmentation of remote sensing images is still challenging. In this paper, we propose a novel multiscale image generation network (MIGN) that can efficiently generate high-resolution segmentation results by considering both details and boundary information. In particular, a multi-attention mechanism method for semantic segmentation of remote sensing images is designed. The attention weight is calculated by capturing the interaction of cross dimensions in a two-branch structure, which can learn the underlying feature information and guarantee the performance of each pixel feature for final classification. We also propose an edge supervised module to ensure that the segmentation boundary has a more accurate performance. A multiscale image fusion algorithm based on the Bayes model is proposed to improve the accuracy of the segmentation module. The performance of our model is evaluated on the ISPRS Vaihingen and Potsdam datasets. The results show that our method is superior to the most advanced image segmentation methods in terms of MIoU and pixel accuracy.

Cross-domain recommendation is an effective way to cope with the cold-start problem in recommendation systems. Knowledge of the current, particularly reviews, is taken into account to improve user/item embedding to reduce the neg-ative transfer that occurs during mapping processes across the source and target domains. Traditional approaches, on the other hand, typically apply review information from the source and target domain independently without consideration of user preference divergence. In this paper, we propose a novel Review-based Preference Invariance Transfer Network (RPITN) to minimize negative transfer by combining reviews from two domains. We first build a review preference invari-ance (RPI) embedding procedure to express user/item review correlations between two domains. Then, to improve the gen-eralization ability of user/item embedding and prevent negative transfer across domains, we carefully insert RPI into the embedding learning and mapping process. Extensive exper-iments on real-world datasets demonstrate the superiority of RPITN compared with other recommendation methods.

As we all know, remote sensing (RS) images contain multi-scale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, proximity relations of same-scale objects, and inclusion relations of cross-scale objects. However, the existing semantic segmentation methods have never explored the cross-scale relations, which are especially important when comes to the situation that the RS objects cannot be accurately identified, they could be supplemented by the surrounding contents. To address the above concern, we propose a scale-relation joint decoupling network (SRJDN) for the semantic segmentation of RS images by simultaneously considering decoupling scales and decoupling relations to excavate more complete relationships of multi-scale RS objects. The SRJDN is performed by following three steps, namely scale decoupling (SD), relation decoupling (RD), and fine-granularity guided fusion (FGF). The SD module uses dilated convolution with different rates to decouple RS objects into different scale feature groups, from small to large scales. Afterward, the RD considers all the spatial topological relationships and decouples these relationships according to the scale, which is divided into two parts, including same-scale relation extraction (SSRE) and cross-scale relation extraction (CSRE). The SSRE establishes the graph structures at each scale independently to mine the relationships of same-scale RS objects and the CSRE constructs the graph in a unified pattern between cross-scales to explore cross-scale target relationships. Third, the FGF module regards small-scale features as fine-granularity representation and applies its attention map to guide the learning of other scale features, which could mine more reliable and comprehensive saliency information and improve the feature consistency. Numerical experiments conducted on two large-scale fine-resolution RS image datasets empirically demonstrate the robustness of the proposed joint decoupling strategy and the effectiveness of the fine-granularity guided fusion in RS image semantic segmentation tasks.