The high‐level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re‐identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re‐identification by combining pedestrian attributes and identities. The multi‐task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC‐reID and Market‐1501, which are commonly used in pedestrian recognition tasks. On the Market‐1501 dataset, the final effect of the algorithm model CMC‐1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC‐reID dataset, CMC‐1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.

With the rapid development of the internet, network security threats are becoming increasingly complex and diverse, making traditional intrusion detection systems (IDSs) inadequate for handling the growing variety of sophisticated attacks. In particular, traditional methods based on rule matching and manual feature extraction demonstrate significant limitations in dealing with small samples and unknown attacks. This paper proposes an intrusion detection system based on multi-level feature extraction and inductive learning (MFEI-IDS) to address these challenges. The model innovatively integrates Fully Convolutional Networks (FCNs) with the Transformer architecture (FCN–Transformer) for feature extraction and utilizes an inductive learning component for efficient classification. The FCN–Transformer Encoder extracts multi-level features from raw network traffic, capturing local spatial patterns and global temporal dependencies, significantly enhancing the representation of network traffic while reducing reliance on manual feature engineering. The inductive learning module employs a dynamic routing mechanism to map sample feature vectors into robust class vector representations, achieving superior generalization when detecting unseen attack types. Compared to existing FCN–Transformer models, MFEI-IDS incorporates inductive learning to handle data imbalance and small-sample scenarios. Experiments on ISCX 2012 and CIC-IDS 2017 datasets show that MFEI-IDS outperforms mainstream IDS methods in accuracy, precision, recall, and F1-score, excelling in cross-dataset validation and demonstrating strong generalization capabilities. These results validate the practical potential of MFEI-IDS in small-sample learning, unknown attack detection, and dynamic network environments.

Remote sensing cross-modal text-image retrieval constitutes a pivotal component of multi-modal retrieval in remote sensing, central to which is the process of learning integrated visual and textual representations. Prior research predominantly emphasized the overarching characteristics of remote sensing images, or employed attention mechanisms for meticulous alignment. However, these investigations, to some degree, overlooked the intricacies inherent in the textual descriptions accompanying remote sensing images. In this paper, we introduce a novel cross-modal retrieval model, specifically tailored for remote sensing image-text, leveraging attention correction and filtering mechanisms. The proposed model is architected around four primary components: an image feature extraction module, a text feature extraction module, an attention correction module, and an attention filtering module. Within the image feature extraction module, the Visual Graph Neural Network (VIG) serves as the principal encoder, augmented by a multi-tiered node feature fusion mechanism. This ensures a comprehensive understanding of remote sensing images. For text feature extraction, both the Bidirectional Gated Recurrent Unit (BGRU) and the Graph Attention Network (GAT) are employed as encoders, furnishing the model with an enriched understanding of the associated text. The attention correction segment minimizes potential misalignments in image-text pairings, specifically by modulating attention weightings in cases where there’s a unique correlation between visual area attributes and textual descriptors. Concurrently, the attention filtering segment diminishes the influence of extraneous visual sectors and terms in the image-text matching process, thereby enhancing the precision of cross-modal retrieval. Extensive experimentation carried out on both the RSICD and RSITMD datasets, yielded commendable results, attesting to the superior efficacy of the proposed methodology in the domain of remote sensing cross-modal text-image retrieval.