Clothing-changing person re-identification (CC-ReID) aims to address cross-camera person identification challenges caused by variations in pedestrian attire, making it a highly valuable research area within computer vision. Due to the high cost of labeling data, unsupervised learning methods have gained significant attention for CC-ReID tasks. However, existing unsupervised methods frequently suffer from high pseudo-label noise and reliance on complex preprocessing (e.g., human parsing) or multi-encoder architectures, resulting in increased computational overhead and deployment difficulties. To tackle these challenges, this paper proposes an end-to-end unsupervised CC-ReID framework named Multi-Scale Adaptive Clustering and Local Consistency Learning (MALC). Utilizing a single CLIP Vision-Encoder as its backbone, MALC discards such intricate procedures. Its core innovations include a multi-scale adaptive density clustering (MS-ADC) strategy to improve pseudo-label quality, and a local consistency learning (LCL) approach that imposes constraints on local region features to enhance robustness against clothing variations. Through the joint optimization of global and local losses, the model learns a highly discriminative and robust feature representation. Experimental results demonstrate that MALC, employing only RGB images, substantially outperforms comparable unsupervised approaches that rely on additional parsing information, showcasing notable advantages in identification accuracy and ease of deployment. The code will be made available at: https://github.com/ykding666/MALC.

In this paper, we propose an innovative relay-aided physical layer security scheme leveraging cooperative transmission and jamming that operates effectively in time-division-multiple-access-based multi-user visible light communication (MuVLC) systems with obstructed communication links. First, to circumvent obstacles between the light source (LS) and legal receivers, we utilize a transmission relay (TR) to forward information signals from the LS to the receiver plane. Subsequently, to ensure communication security, we select a jamming relay to enlarge the channel capacity gap between eavesdroppers and legal receivers through cooperative jamming. Using stochastic geometry, we derive closed-form expressions for key performance metrics, including the cumulative distribution function of secrecy capacity, security outage probability, and their lower and upper bounds, both with and without relay cooperation. To further enhance system security, we introduce a disk-shaped security-protected zone around the TR. All analytical expressions are numerically verified through Monte Carlo simulations, serving as benchmarks to evaluate security performance. Numerical results confirm that our proposed scheme can ensure continuous signal reception and high-level security. Furthermore, while relay cooperation modestly enhances system security, the introduction of a security-protected zone around the TR yields substantial security improvement, which offers practical insights for designing secure time-division-multiple-access-based MuVLC where communication links are obstructed.

With the rapid development of the Visual Internet of Things (VIoT), person re-Identification (ReID) technology has made significant strides in research, particularly in the domains of urban security and intelligent surveillance. However, cloth-changing person re-identification (CC-ReID) poses significant challenges to traditional appearance-based methods due to frequent changes in clothing. To address this issue, this paper proposes a Person Parsing-Driven and Text-Guided for Cloth-Changing Person Re-identification (PT-ReID). This approach optimizes the image encoder of the CLIP model through a multi-branch design and leverages person parsing techniques to extract stable, clothing-invariant biological features. Additionally, the method incorporates Context Optimization (CoOp) technology, using learnable text descriptions as soft supervision to enhance cross-modal alignment. A saliency region loss mechanism is also introduced to minimize discrepancies between different feature representations, further improving the model’s ability to learn discriminative pedestrian features. Experimental results on three public datasets—PRCC, LTCC, and VC-Clothes—demonstrate that PT-ReID significantly outperforms state-of-the-art methods in Rank-1 accuracy and mean Average Precision (mAP) under cloth-changing scenarios, proving its effectiveness and robustness in handling complex clothing variations.

The dynamic conditions and internal states of portable energy storage system (PESS), such as temperature, electricity price, state of charge (SOC), and state of health (SOH), significantly impact battery degradation. Current decision-making models for PESS operation often oversimplify the modeling of battery degradation. To address this, we introduce an environment-adaptive online learning framework that effectively integrates deep neural networks and reinforcement learning to exploit and explore external environments (i.e., electricity prices and temperature) and internal dynamics (i.e., battery degradation), providing decision support for PESS operation. This framework dynamically updates battery degradation and decision-making models in real-time, enhancing adaptive responses to external changes. Specifically, we developed a neural network based on porous electrode theory that considers multi-physical factors, such as charging power, initial and terminal SOC, SOH, and temperature to accurately assess battery degradation. This network is embedded within a deep reinforcement learning algorithm, enabling real-time, adaptive decision-making for PESS amidst varying environmental conditions. Furthermore, to navigate complex operational environments, a fine-tuning mechanism is incorporated into the degradation neural network. Application of this framework to the energy arbitrage of PESS in the California power grid demonstrates an average benefit increase of 37% compared to traditional degradation assessment models. Note to Practitioners—In this work, we develop a novel approach to addressing the critical issue of battery degradation in PESS. Existing models often oversimplify degradation, hindering accurate assessments of performance and lifespan projections. More recently, learning-based algorithms have demonstrated outstanding performance in both battery degradation modeling and real-time decision-making. In this sense, we introduce a sophisticated neural network model grounded in a porous electrode model. This model considers multi-physics factors involving charging/discharging power, initial and terminal SOC, SOH, and temperature. Complementing this, we integrate the aforementioned model into an online learning framework, enabling real-time decision-making for PESS. Furthermore, to tackle the challenges of complex operating environments, a fine-tuning mechanism is incorporated into the battery degradation neural network. We validate the effectiveness of the proposed methods through an energy arbitrage application of PESS and also reveal its potential for on-demand applications in energy and transportation systems. This note anticipates a positive impact on PESS management and contributes significantly to the evolution of energy storage systems, offering practitioners invaluable decision support for commercial applications involving battery sharing, trading, and renting.

Person re-identification plays a crucial role in various domains, such as intelligent transportation, public safety, and smart cities. However, the current person re-identification methods are unable to address the challenges posed by long-term clothes-changing scenarios. As a result, clothes-changing person re-identification has gained increasing attention and is considered a highly challenging task. Most existing works in this area mainly focus on learning from multi-modal information such as outlines and sketches, while overlooking valuable features in the original RGB images. In this paper, we propose a novel method that conducts in-depth research on RGB images. We introduce the Scaled Triplet Loss (STL) for metric learning, which helps similar samples be closer in the feature space and encourages the model to pay more attention to the distances between samples that are difficult to distinguish. In addition, over an existing loss function, we propose Augmentation Clothing-irrelevant Features Loss (ACL), which better exploits features unrelated to clothing in RGB images, resulting in more advanced model performance. Extensive experiments have demonstrated the performance of our model surpasses other state-of-the-art methods in two real-world datasets.

In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant. However, this task faces considerable challenges due to appearance changes caused by clothing alterations. Addressing this issue, this paper proposes an innovative method for disentangled feature extraction, effectively extracting discriminative features from pedestrian images that are invariant to clothing. This method leverages pedestrian parsing techniques to identify and retain features closely associated with individual identity while disregarding the variable nature of clothing attributes. Furthermore, this study introduces a gated channel attention mechanism, which, by adjusting the network’s focus, aids the model in more effectively learning and emphasizing features critical for pedestrian identity recognition. Extensive experiments conducted on two standard CC-ReID datasets validate the effectiveness of the proposed approach, with performance surpassing current leading solutions. The Top-1 accuracy under clothing change scenarios on the PRCC and VC-Clothes datasets reached 64.8% and 83.7%, respectively.

This study focuses on the Clothes-Changing Person Re-Identification (CC-ReID) problem, aiming to achieve precise recognition of the same pedestrian despite changes in attire. Despite some progress in this field, challenges persist in maintaining pedestrian identity consistency due to variations in clothing, leading to recognition disturbances. To address this, we propose a novel Multidimensional Semantic Disentanglement Network (MSD-Net). This network enhances the recognition capability for non-clothing areas by reducing reliance on clothing features and integrating discriminative and global features. Specifically, we employ semantic segmentation maps for pedestrian feature disentanglement, combined with RGB images, to effectively erase clothing features and consequently enhance focus on non-clothing areas. Additionally, we introduce a method to convert pedestrian semantic segmentation maps into dual-precision feature maps, utilizing a spatial attention mechanism to proactively learn distinctive pedestrian features, thereby further improving model performance. Extensive experiments on two standard CC-ReID datasets validate the effectiveness of our approach, outperforming existing state-of-the-art solutions. On the PRCC and VC-Clothes datasets, our model achieves Top-1 accuracies of 65.3% and 84.1%, respectively, in clothes-changing scenarios.

Traditional text-based person re-identification (ReID) techniques heavily rely on fully matched multi-modal data, which is an ideal scenario. However, due to inevitable data missing and corruption during the collection and processing of cross-modal data, the incomplete data issue is usually met in real-world applications. Therefore, we consider a more practical task termed the incomplete text-based ReID task, where person images and text descriptions are not completely matched and contain partially missing modality data. To this end, we propose a novel Prototype-guided Cross-modal Completion and Alignment (PCCA) framework to handle the aforementioned issues for incomplete text-based ReID. Specifically, we cannot directly retrieve person images based on a text query on missing modality data. Therefore, we propose the cross-modal nearest neighbor construction strategy for missing data by computing the cross-modal similarity between existing images and texts, which provides key guidance for the completion of missing modal features. Furthermore, to efficiently complete the missing modal features, we construct the relation graphs with the aforementioned cross-modal nearest neighbor sets of missing modal data and the corresponding prototypes, which can further enhance the generated missing modal features. Additionally, for tighter fine-grained alignment between images and texts, we raise a prototype-aware cross-modal alignment loss that can effectively reduce the modality heterogeneity gap for better fine-grained alignment in common space. Extensive experimental results on several benchmarks with different missing ratios amply demonstrate that our method can consistently outperform state-of-the-art text-image ReID approaches.