RGBT target tracking is a significant downstream task in the field of object tracking. However, compared to visible light target tracking, RGBT target tracking faces the challenge of smaller datasets, making it difficult to achieve performance levels comparable to those achieved in visible light target tracking. To address how to effectively combine the complementary characteristics of visible and thermal modalities, as well as how to fully leverage the superior performance of models trained on visible light target tracking tasks, while also aiming for lower computational costs and higher tracking effectiveness, a dual-prompt complementary fusion strategy for an RGBT tracking network is proposed. Drawing on the concept of prompt learning, this network aims to extend the efficient performance of visible light target tracking to the RGBT target tracking domain. In its implementation, the prompt module inputs both visible and thermal modality information as dual prompts into the backbone network, where the network utilizes these prompts to generate new, enriched prompt information at each layer. Subsequently, an information enhancement fusion module enhances the acquired prompt information and refeeds it into the backbone network, aiming to improve the tracking accuracy and robustness. Experimental results on GTOT, RGBT234 and LasHeR datasets show that the tracking accuracy (PR) and success rate (SR) of the network reach 93.1%/76.8%, 84.4%/62.4% and 66.8%/53.8%, respectively, which is improved compared with the current mainstream RGBT target tracking network, which verifies the effectiveness of the network.

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering.

ABSTRACT Convolutional neural network (CNN)‐based auxiliary diagnostic systems have been widely proposed. However, CNNs have limitations in perceiving global features and more subtle features, which makes existing methods unable to achieve ideal accuracy in tasks such as pneumoconiosis screening. To overcome these limitations, we propose MBSM‐Net, a new multi‐branch structure‐enhanced model for pneumoconiosis screening and grading based on X‐ray images. MBSM‐Net introduces an adaptive feature selection and fusion module to achieve synchronous extraction and hierarchical fusion of global and local features. In the local feature extraction module, we designed a CNN‐Mamba module. This module integrates prior information through a detailed enhancement module to compensate for the shortcomings of traditional convolutions and significantly enhances the expression of subtle lesion information. Meanwhile, the Mamba module simulates pixel‐level long‐range dependencies to extract finer‐grained texture features. In the global feature extraction module, we cleverly utilize the windowed multi‐head self‐attention (W‐MSA) mechanism, enabling the model to better understand the overall distribution and degree of fibrosis of pulmonary lesions. We validated the MBSM‐Net model on 1,760 real anonymized patient X‐ray chest films. The results showed that the accuracy of the MBSM‐Net model reached 78.6%, and the F 1 score reached 79%, both of which are superior to existing models.

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

Locality-based feature learning has drawn more and more attentions recently. However, most of locality-based feature learning methods only consider a kind of local neighbor information, and such the locality-based methods are difficult to well reveal intrinsic geometrical structure of raw high-dimensional data. In this paper, we propose a novel multi-locality correlation feature learning algorithm for multi-view data, called multi-locality discrimination canonical correlation analysis (MLDCCA), which can learn nonlinear correlation features with strong discriminative power. Different from the locality-based methods, our algorithm not only employs multiple local patches of each raw data to well capture the intrinsic geometrical structure information, but also fully considers intraclass scatter information for further enhancing the class separability of the learned correlation features. Extensive experimental results on several real-word image datasets have demonstrated the effectiveness of our algorithm.