Although diffusion-based image virtual try-on has made considerable progress, emerging approaches still struggle to effectively address the issue of hand occlusion (i.e., clothing regions occluded by the hand part), leading to a notable degradation of the try-on performance. To tackle this issue widely existing in real-world scenarios, we propose VTON-HandFit, leveraging the power of hand priors to reconstruct the appearance and structure for hand occlusion cases. Firstly, we tailor a Hand-Pose Aggregation Net using the ControlNet-based structure explicitly and adaptively encoding the global hand and pose priors. Besides, to fully exploit the hand-related structure and appearance information, we propose Hand-feature Disentanglement Embedding module to disentangle the hand priors into the hand structure-parametric and visual-appearance features, and customize a masked cross attention for further decoupled feature embedding. Lastly, we customize a hand-canny constraint loss to better learn the structure edge knowledge from the hand template of model image. VTON-HandFit outperforms the baselines in qualitative and quantitative evaluations on the public dataset and our self-collected hand-occlusion Handfit-3K dataset particularly for the arbitrary hand pose occlusion cases in real-world scenarios. Our project page is at: https://vton-handfit.github.io.

As a cornerstone for enabling intelligent transportation systems, traffic flow prediction provides a technical basis for urban planning, traffic management and travel management. Federated learning (FL) is frequently employed in traffic flow prediction to meet privacy requirements. However, the traditional FL paradigm is subject to several limitations, including data heterogeneity, unbalanced data volume, and limited available resources. To address these problems, we propose a three-tiered hierarchical federated meta-learning framework. Firstly, the client will use a soft gap sampler (SGS) to sample personalized parameters, thereby alleviating bandwidth pressure and obtaining the gradients required for backpropagation of the global model on the query set. Secondly, these parameters and gradients are respectively used in the regional server and the global server to obtain regional parameters with regional awareness ability and global parameters with learning ability. Finally, the regional server will fuse the two parameters for the initial parameters in the prediction stage. A large number of experiments prove that the proposed framework can cope with the integration of cross-regional models, the adaptation of models in data-scarce regions, and different bandwidth limitations of clients.

Despite remarkable progress in image-based virtual try-on systems, generating realistic and robust fitting images for cross-category virtual try-on remains a challenging task. The primary difficulty arises from the absence of human-like reasoning, which involves addressing size mismatches between garments and models while recognizing and leveraging the distinct functionalities of various regions within the model images. To address this issue, we draw inspiration from human cognitive processes and disentangle the complex reasoning required for cross-category try-on into a structured framework. This framework systematically decomposes the model image into three distinct regions: try-on, reconstruction, and imagination zones. Each zone plays a specific role in accommodating the garment and facilitating realistic synthesis. To endow the model with robust reasoning capabilities for cross-category scenarios, we propose an iterative data constructor. This constructor encompasses diverse scenarios, including intra-category try-on, any-to-dress transformations (replacing any garment category with a dress), and dress-to-any transformations (replacing a dress with another garment category). Utilizing the generated dataset, we introduce a tri-zone priors generator that intelligently predicts the try-on, reconstruction, and imagination zones by analyzing how the input garment is expected to align with the model image. Guided by these tri-zone priors, our proposed method, CrossVTON, achieves state-of-the-art performance, surpassing existing baselines in both qualitative and quantitative evaluations. Notably, it demonstrates superior capability in handling cross-category virtual try-on, meeting the complex demands of real-world applications.

Robust registration of multimodal remote sensing images (MRSIs) remains a challenge due to nonlinear intensity differences (NIDs) and geometric distortions. Existing methods typically rely on maximum index maps (MIMs) to construct feature descriptors. However, MIM is sensitive to rotation variations, under which circumstance the number of correct matches decreases under severe geometric distortions. To address these problems, this study proposes an ingenious registration method (named as CSFD-AFM) based on a cyclic shift feature descriptor (CSFD) and an adaptive feature matching (AFM). The CSFD is a rotation-invariant descriptor that enables efficient orientation adjustment through cyclic shifts of a three-dimensional feature array constructed from a modified MIM. To achieve rotation invariance, a dominant-orientation-based method for cyclic shift is introduced. Based on the CSFD, we devise a matching strategy named AFM, which adaptively adjusts the scale and orientation of CSFDs for robust and accurate feature matching. Experiments were conducted on six MRSI datasets to evaluate the performance of the proposed CSFD-AFM. The results suggest that the proposed CSFD-AFM outperforms the existing seven state-of-the-art methods including ReDFeat, MINIMA-LG, RIFT2, MS-HLMO, HOWP, POS-GIFT, and GLS-MIFT. In addition, our proposed CSFD-AFM achieves the accuracy within 2 pixels while maintaining its robustness against severe geometric distortions such as large rotations.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

Abstract The target of Emotion modeling is to establish an system that can perceive, recognize, and express emotions with concurrency which humanity have by proper mathematical models. A big challenge in Emotion modeling is to establish the complex personal system of an intelligent agent or machine in a quasi‐physical and quasi‐sociological way, so that it rationally responds to emotions and behaviors by different internal and external stimuli with concurrency. For this purpose, an emotion classification model is presented for the audio‐visual external stimuli based on an improved long short‐term memory network. Then, based on Gross's emotional regulation theory, a hidden Markov model is constructed to imitate the process framework of human emotional cognition and personalized emotional regulation and expression, so as to realize the machine's intuitive and reasonable response to stimuli with concurrency. In this article, a framework of machine personalized artificial emotion simulation is preliminarily constructed, which provides a new model for human–computer interaction, in order to provide a reference solution for machine emotion understanding and expression.

In harsh and complex electric power system environments of Smart Grid, the observed signal is always disturbed by multiple signal sources, environmental noise or sudden packet loss, generating a significant challenge for reliable remote monitoring. In this paper, we propose a new post-processing detecting scheme for wireless data transmission in the distributed monitoring system to highly tolerate these interferences after transmission procedure and exactly reconstruct the observed signal from rough data in the sink center. Hardware or algorithm implementation to prevent the possible failures is not necessary in sensors, which perfectly solves the problem about changing batteries or updating software for practical application with thousands of energy-constrained sensors. The sink center with high computational capability and abundant energy resource decides the tolerance of interference and retransmission condition, reconstructs the observed signal from the mixing sensed data of sensors by the estimation of frequency domain amplitude via compressed sensing with regarding frequency domain amplitude of the interference signal as 0. In the proposed scheme, we implement compressed sensing with an l1-l2 optimization, where the cost function is defined as a sum of l1 and l2 norms with a mixing parameter, which enables us to control the threshold between the observed signal and the interference ones.

A computer aided network evaluation and optimization platform is presented to help the development of WSN application. This middleware together with the existing network simulators replace the function of field test during the process of WSN designs. The proposed platform reduces engineering cost and increases development efficiency. Furthermore, the network optimization is implemented in the platform to provide an automatic design of WSN application.