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.

Dialogue Aspect-based Sentiment Quadruple analysis (DiaASQ) extends ABSA to more complex real-world scenarios (i.e., dialogues), which makes existing generation methods encounter heightened noise and order bias challenges, leading to decreased robustness and accuracy.To address these, we propose the Segmentation-Aided multi-grained Denoising and Debiasing (SADD) method.For noise, we propose the Multi-Granularity Denoising Generation model (MGDG), achieving word-level denoising via sequence labeling and utterancelevel denoising via topic-aware dialogue segmentation.Denoised Attention in MGDG integrates multi-grained denoising information to help generate denoised output.For order bias, we first theoretically analyze its direct cause as the gap between ideal and actual training objectives and propose a distribution-based solution.Since this solution introduces a one-to-many learning challenge, our proposed Segmentationaided Order Bias Mitigation (SOBM) method utilizes dialogue segmentation to supplement order diversity, concurrently mitigating this challenge and order bias.Experiments demonstrate SADD's effectiveness, achieving state-ofthe-art results with a 6.52% F1 improvement.

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.

ASTE (Aspect Sentiment Triplet Extraction) has gained increasing attention.Recent advancements in the ASTE task have been primarily driven by Natural Language Generationbased (NLG) approaches.However, most NLG methods overlook the supervision of the encoder-decoder hidden representations and fail to fully utilize the semantic information provided by the labels to enhance supervision.These limitations can hinder the extraction of implicit aspects and opinions.To address these challenges, we propose a tagging-assisted generation model with encoder and decoder supervision (TAGS), which enhances the supervision of the encoder and decoder through multipleperspective tagging assistance and label semantic representations.Specifically, TAGS enhances the generation task by integrating an additional sequence tagging task, which improves the encoder's capability to distinguish the words of triplets.Moreover, it utilizes sequence tagging probabilities to guide the decoder, improving the generated content's quality.Furthermore, TAGS employs a selfdecoding process for labels to acquire the semantic representations of the labels and aligns the decoder's hidden states with these semantic representations, thereby achieving enhanced semantic supervision for the decoder's hidden states.Extensive experiments on various public benchmarks demonstrate that TAGS achieves state-of-the-art performance.

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.