Traditional single neural network-based geo-localization methods for cross-view imagery primarily rely on polar coordinate transformations while suffering from limited global correlation modeling capabilities. To address these fundamental challenges of weak feature correlation and poor scene adaptation, we present a novel framework termed ICT-Net (Integrated CNN-Transformer Network) that synergistically combines convolutional neural networks with Transformer architectures. Our approach harnesses the complementary strengths of CNNs in capturing local geometric details and Transformers in establishing long-range dependencies, enabling comprehensive joint perception of both local and global visual patterns. Furthermore, capitalizing on the Transformer’s flexible input processing mechanism, we develop an attention-guided non-uniform cropping strategy that dynamically eliminates redundant image patches with minimal impact on localization accuracy, thereby achieving enhanced computational efficiency. To facilitate practical deployment, we propose a deep embedding clustering algorithm optimized for rapid parsing of geo-localization information. Extensive experiments demonstrate that ICT-Net establishes new state-of-the-art localization accuracy on the CVUSA benchmark, achieving a top-1 recall rate improvement of 8.6% over previous methods. Additional validation on a challenging real-world dataset collected at Beihang University (BUAA) further confirms the framework’s effectiveness and practical applicability in complex urban environments, particularly showing 23% higher robustness to vegetation variations.

With the rapid development of artificial intelligence, dialogue systems have become a prominent form of human-computer interaction. However, traditional centralized or fully local training approaches face challenges in balancing privacy preservation and personalization due to data privacy concerns and heterogeneous device capabilities. Federated learning, as a representative distributed paradigm, offers a promising solution. However, existing methods often suffer from overfitting under limited client data and tend to forget global information after multiple training rounds, leading to poor generalization. To address these issues, we propose FedDTRE, a Federated adaptive aggregation strategy for Dialogue generation based on Trustworthiness Evaluation. Instead of directly replacing local models with the global model, FedDTRE leverages trustworthiness scores of both global and local models on a fairness-oriented evaluation dataset to dynamically regulate the global model's contribution during local updates. Experimental results demonstrate that FedDTRE can improve dialogue model performance and enhance the quality of dialogue generation.

Infrared target detection supports the continuous observation of traffic participants and low-altitude targets across aerial and fixed-view imaging settings, particularly under weak or changing illumination. However, infrared targets are often small, weakly textured, and easily confused with thermal noise and background clutter. Large pretrained vision models offer strong representation and generalization capabilities, but their parameter counts and computational costs make direct deployment on edge platforms with limited resources impractical. To transfer these capabilities to lightweight models, this paper proposes knowledge distillation at the label level based on filtered teacher detections. A large teacher adapted to the infrared domain first generates candidate boxes. Candidate boxes are selected using confidence thresholds, class reliability, and spatial relationships with ground truth annotations. The retained teacher boxes and the original annotations jointly form the student training targets, while the student retains its standard detection loss and original inference structure. On an independent sequence-level test set, the YOLOv5n baseline obtains an mAP@0.5 of 0.2277 and an mAP@0.5:0.95 of 0.1070, whereas filtered label distillation obtains 0.2547 and 0.1153, respectively, over three matched seeds. The filtering rules and thresholds are fixed before this evaluation, and the fixed Epoch-30 checkpoint is used for every run. Both student architectures are deployed on RK3588.