Unmanned aerial vehicles (UAVs) have garnered significant attention due to their operational flexibility, enabling expanded application scenarios across diverse fields. The Text-Based Pedestrian Retrieval (TBPR) task aims to identify corresponding images from textual descriptions, yet existing research has primarily focused on ground-level views. To broaden the applicability of TBPR systems, we introduce aerial-view analysis and propose a novel Text-Based Aerial Pedestrian Retrieval (TBAPR) task. This task introduces unique challenges, particularly the dual gaps in cross-view (aerial vs. ground) and cross-modal (text vs. image) matching, which are more complex than traditional TBPR or aerial-ground pedestrian understanding tasks. To address these challenges, we propose an Adaptive Elastic Alignment Network with FIne-Grained Representation Mining (AEA-FIRM). Our framework tackles the cross-view gap through an AEA loss that adaptively prioritizes critical semantic features while dynamically aligning textual and aerial semantics under challenging conditions. Concurrently, the FIRM module refines visual-linguistic representations by mining fine-grained pedestrian attributes and explicitly textualizing them for cross-modal matching verification. Extensive experiments demonstrate that AEA-FIRM achieves state-of-the-art performance, outperforming existing TBPR methods by 4.87% in Rank-1 accuracy. Our code and dataset are available at https://github.com/xbdxwyh/AEA-FIRM-main.git.

Time-series data are vital for preserving earthen ruins and evaluating wind erosion effects. Harsh conditions at these sites often lead to sensor degradation and significant data gaps. To tackle wind speed data imputation for such environments, we introduce a Multi-Scale Conditional Generative Adversarial Network (MSC-GAN) with a Transformer-based generator. This model integrates features across hourly, daily, and weekly scales, combined with real-time wind direction data and random noise. Utilizing the Transformer’s ability to model long-range dependencies and multi-scale information, MSC-GAN adeptly manages complex missing data patterns. We validate MSC-GAN using nearly two years of near-surface wind speed data from the Suoyang City earthen ruins. Our experimental results reveal that MSC-GAN substantially improves imputation accuracy—by 40.2% for short gaps and 6.7% for long gaps—over traditional methods. Code is available at https://github.com/zizhou001/msc-gan.

Mobile edge computing (MEC) enabled artificial intelligence-generated content (AIGC) has garnered considerable attention. To support AIGC metaverse applications within MEC in Internet of Things (IoT) networks, it is effective to offload computation tasks, particularly those involving neural networks generative in AIGC, from mobile devices to edge clouds. Existing solutions typically assume the availability of a dedicated and powerful edge server for each user with single modal data, which can handle the entire AIGC service offloading. However, the practical availability of such dedicated and powerful servers may be limited, necessitating the utilization of less capable alternatives. Thus, we propose the multimodal parallel offloading AIGC framework which partitions multimodal content and offloads partial diffusion tasks to multiple servers. Our proposed scheme accelerates mobile deep vision multimodal metaverse applications through parallel offloading provided by multiple servers. We further utilize the generative AI scheme to solve offloading problems to adapt the dynamic and available communication and computing resource in wireless IoT network. Our framework proposed a multimodal parallel diffusion offloading scheme with integrating the recurrent region proposal prediction algorithm to optimize communication and computing resources while minimizing delay. Simulation results show that our approach can significantly reduce delay compared to conventional algorithms.

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks.

On-device deep learning enables mobile devices to perform complex tasks, such as object detection and voice translation, regardless of the network condition. The advanced deep learning model gives an excellent performance, also leads to a heavy burden on resource-limited devices (i.e., mobile devices). To speed up the on-device deep learning. Prior studies focus on developing lightweight network architecture for real-time inference by sacrificing model accuracy. This paper presents ATO-EDGE: adaptive task offloading for deep learning based on edge computing. Considering three optimization goals, energy consumption, accuracy, and latency, ATO-EDGE leverages an offline pre-trained model to select a suitable deep learning model on a specific device to process the given task. We apply our approach to object detection and evaluate it on Jetson TX2, Xilinx ZYNQ 7020, and Raspberry 3B+. The deep learning model candidates contain ten typical object detection models trained on Microsoft COCO 2017 dataset. We obtain, on average, 28.25%, 35.44%, and 0.9 improvements respectively for latency, energy consumption, and mAP (mean average precision) when compared to the SOTA DETR model on the Raspberry Pi.