Aiming to match the person identity between daytime VISible (VIS) and nighttime Near-InfraRed (NIR) images, VIS-NIR re-identification (Re-ID) has attracted increasing attention due to its wide applications in low-light scenes. However, dramatic modality discrepancies between VIS and NIR images lead to a considerable intra-class gap in the feature space, which impacts identity matching. To bridge the modality gap, we propose a Tri-level Modality-information Disentanglement (TMD) to disentangle modality information at the levels of raw image, features distribution and instance features. Our model consists of three key modules, including Style-Aligned Converter (SAC), Two-Steps Wasserstein Loss (TSWL) and Self-supervised Orthogonal Disentanglement (SOD) to handle the modality information at the three levels. Firstly, aiming at reducing modality discrepancy at image-level, the SAC is introduced to generate style-aligned images by the designed style converter and $\mathcal {A}$-distance learning approach. The SAC can effectively alleviate the style discrepancy between VIS and NIR images with a negligible increase in model complexity. Secondly, considering the heterogeneity of VIS and NIR feature distribution caused by the structure- and style-misaligned raw images, we propose the TSWL to decrease the VIS-NIR gap at distribution-level by two distribution alignment steps. Specifically, after generating style-consistent images, we eliminate modality-related discrepancy by aligning the distribution between structure-aligned original and generated VIS/NIR images and bridge the modality-unrelated gap by aligning the style-consistent generated VIS-NIR images. Thirdly, focusing on further reducing the modality discrepancy at instance-level, the SOD is presented to construct orthogonal constraints between the extracted modality- and identity-related features. Since the modality-related factors are disentangled from the instance features, the proposed TMD efficiently learns the modality-unrelated and identity-discriminative representations, which are productive to conduct person Re-ID task on the VIS-NIR images. Comprehensive experiments are carried out on two cross-modality pedestrian Re-ID datasets to demonstrate the effectiveness of TMD.

Tri-level Modality-information Disentanglement for Visible-Infrared Person Re-Identification

Consensus overmulti-agent networks can be accelerated by utilizing agent’s memory to the control protocol. In this paper, a general protocol with memory information from the node and its neighbors is designed. We aim to provide an optimal memory scheme to accelerate consensus. The contributions of this paper include: (i) For the one-tap memory scheme, we prove that the memory information of neighbors is unnecessary for the optimal convergence. (ii) It is proved that in the worst-case scenario, one-tap node memory is sufficient to achieve the optimal convergence rate, that is, adding more taps of past information from neighbors can not improve the rate further. (iii) It is found that the convergence rate with two-tap memory can be further improved on star networks. Numerical examples are presented to illustrate the validity and correctness of the obtained results. 

/pdf/optimal-memory-scheme-for-accelerated-consensus-over-multi-22sg8crb.pdf

Optimal Memory Scheme for Accelerated Consensus Over Multi-Agent Networks

Dynamic Quality-of-Service (QoS) data can be efficiently represented by a Non-negative Latent-factorization-of-tensors model, which relies on a Non-negative and Multiplicative Update on Incomplete Tensors (NMU-IT) algorithm. Nevertheless, NMU-IT frequently encounters slow convergence and inefficient hyper-parameters selection. Targeting at overcome these critical defects, this paper proposed to improve the NMU-IT algorithm from two perspectives: a) integrating a generalized Nesterov's accelerated gradient method to accelerate the resultant model's convergence rate, and b) establishing the hyper-parameter adaptation mechanism through the particle swarm optimization strategy. On the basis of these conceptions, this study successfully builds a Generalized Nesterov's Accelerated Gradient-incorporated Non-negative Latent-factorization-of-tensors (GNL) model for precisely and high-efficiently representing the dynamic QoS data. The proposed GNL model has shown its superiority over several advanced models concerning both the precision of estimating missing QoS data and training efficiency, as demonstrated by the experiments conducted on two dynamic QoS datasets.

A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data

This paper examines the problem of bipartite consensus for Takagi-Sugeno fuzzy multi-agent systems subject to uncertainties. The principal intention of this work is to develop a non-fragile controller through which the considered multi-agent system can achieve bipartite consensus. An undirected signed graph is considered to describe the cooperative and competitive interaction among neighboring agents. On this circumstance, we propose a consensus protocol by the utilization of graph theory. Further, with the aid of Lyapunov stability theory, the sufficient conditions are derived in the form of linear matrix inequalities which ensures the bipartite consensus of the considered multi-agent system. At last, two numerical examples are provided with their simulations to describe the viability of the developed theoretical findings. 

Bipartite Consensus for Takagi-Sugeno Fuzzy Uncertain Multi-Agent Systems With Gain Fluctuations

More robust intelligent transportation systems including autonomous driving systems are in full flourish with the revolution of deep learning and the 6G wireless communication network. Vehicle Re-Identification, an indispensable branch of the intelligent transportation system, aims to retrieve specific vehicles captured from non-overlapping cameras. However, this is fundamentally challenging with the substantial inter-class similarity and substantial intra-class divergence. Embedding semantic information into vehicle re-identification task has gained ample interest, but the performance needs to be further improved. This work proposes a semantic-oriented feature coupling transformer (SOFCT) for vehicle re-identification as a solution. Specifically, the knowledge-based transformer is first embedded to model images with discriminative attributes. Second, original patches are divided into five semantic groups via semantics-patches coupling, and the feature extractions for different semantics are performed in the semantic feature extraction (SFE) transformer. Third, patch features are weighted via semantics-patches coupling in the patch feature weighting (PFW) transformer, the weighted feature is fed into subsequent encoders to excavate information. Finally, two groups of learnable semantics are embedded to automatically learn semantic features in the learnable semantic extraction (LSE) transformer. Experiments demonstrate that the proposed SOFCT method surpasses other state-of-the-arts with the mAP/Rank-1 of 80.7%/96.6%, 89.8%/84.5%, 86.4%/80.9%, and 84.3%/78.7% on VeRi776 and VehicleID. 

Semantic-Oriented Feature Coupling Transformer for Vehicle Re-Identification in Intelligent Transportation System

In this article, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. The intention of this work is to solve large-scale optimization problems where the local objective function is complicated, and numerous. Different from most of the existing works, the local objective function of each agent is presented as the average of finite instantaneous functions. Integrating the gradient tracking algorithm with stochastic averaging gradient technology, a distributed stochastic gradient tracking (termed as S-DIGing) algorithm is proposed. At each time instant, only one randomly selected gradient of an instantaneous function is computed, and applied to approximate the local batch gradient for each agent. Based on a novel primal-dual interpretation of the S-DIGing algorithm, it is shown that the S-DIGing algorithm linearly converges to the global optimal solution when step-size do not exceed an explicit upper bound, and the instantaneous functions are strongly convex with Lipschitz continuous gradients. Numerical experiments are presented to demonstrate the practicability of the S-DIGing algorithm, and correctness of the theoretical results.

S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

As an indispensable part of intelligent transportation system (ITS), vehicle re-identification (Re-ID) aims to retrieve all target vehicle images captured from non-overlapping cameras. However, this task remains very challenging due to the variation of camera perspective and the similar appearance among the vehicles with the same type and color, i.e., large intra-class variances and small inter-class variances. Previous methods have made a great progress on vehicle Re-ID by leveraging local details and aligning local features, this issue is still far from being solved. In this work, we propose to decouple identity-unrelated information from vehicle representation, tackling the problems of camera perspective variation and vehicle appearance similarity. The keypoint of this method is to learn a distinguishable feature embedding that is independent of identity-unrelated information. Specially, the novel Identity-Unrelated Information Decoupling (IUID) paradigm is designed to learn invariant features of the vehicle with the same ID in different scenes. In our approach, identity-unrelated information can be divided into two kinds of information, i.e., camera perspective information and background information. For the former, through a feature-level camera generative adversarial module, we can decouple camera perspective information from the feature embedding after extracting invariant features across different cameras perspective. For the latter, we propose a vehicle-mask transformer to enhance the attention of the model on local details while reducing the impact of background information. Extensive experiments on two public datasets demonstrate the superiority of IUID over the current state-of-the-arts methods.

Identity-Unrelated Information Decoupling Model for Vehicle Re-Identification

Considering the extensive applications of the model predictive control (MPC), this paper investigates the MPC problem with an application to the optimal output consensus of high-order multi-agent systems. A synchronous distributed optimization algorithm for the MPC problem is proposed, and we further devise its asynchronous version. The algorithm allows agents to choose the uncoordinate step-sizes depending on local information, which improves the flexibility of multi-agent systems. The convergence of the proposed algorithm is guaranteed if the positive uncoordinate step-sizes satisfy the explicit conditions. Compared with the synchronous version that needs a global clock to control all agents for communication and update, the asynchronous algorithm does not require each agent in the multi-agent systems to update and communicate simultaneously, and also converges to the optimal global solution to the problem. The numerical simulations demonstrate the availability of the proposed algorithm and the validity of the theoretical results.

Asynchronous Distributed Model Predictive Control for Optimal Output Consensus of High-Order Multi-Agent Systems

The Barzilai–Borwein Method for distributed optimization over unbalanced directed networks

Model-Free Reinforcement Learning Economic Dispatch Algorithms for Price-Based Residential Demand Response Management System

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