SDMA-Net: Swin Transformer-Based Dynamic Memory-Attention Network for Endoscopic Navigation by Runnan Zhang, Qi Tian, Jinghui Chu, Wei Lu

“…Additionally, a Dynamic Memory Augmentation Module (DMAM) adaptively updates and retrieves motion patterns to enhance robustness against noise and occlusions. …”
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Multiscale Hyperspectral Pansharpening Network Based on Dual Pyramid and Transformer by Hengyou Wang, Jie Zhang, Lian-Zhi Huo

“…However, most existing deep learning-based pansharpening methods have some issues, such as spectral distortion and insufficient spatial texture enhancement. …”
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Efficient Attention Transformer Network With Self-Similarity Feature Enhancement for Hyperspectral Image Classification by Yuyang Wang, Zhenqiu Shu, Zhengtao Yu

“…Furthermore, we design two efficient feature extraction modules based on the preprocessed patches, called spectral interactive transformer module and spatial conv-attention module, to reduce the computational costs of the classification framework. …”
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Cross-Modal Object Detection Based on Content-Guided Feature Fusion and Self-Calibration by Liyang Ning, Xuxun Liu, Luoyu Zhou, Xueyu Zou

“…Additionally, deep features are prone to degradation through multiple convolutional layers, leading to the loss of detailed information. To address these issues, we propose a dual-backbone cross-modal object detection model based on YOLOv8n. …”
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ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing by Xiangyue Zheng, Yijuan Qiu, Gang Zhang, Tao Lei, Ping Jiang

“…This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. …”
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Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction by ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang

“…To address the aforementioned issues, an attention-guided multi-task learning network with tumor segmentation as an auxiliary task is proposed for PLNM prediction. …”
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BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images by Mingjin Zhang, Yaofei Li, Jie Guo, Yunsong Li, Xinbo Gao

“…BurgsVO consists of two key modules: the Burgs equation heuristics module, which facilitates feature extraction, and the average diagonal vertex offset (ADVO) encoding scheme, which significantly reduces computational costs. …”
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A texture enhanced attention model for defect detection in thermal protection materials by Jialin Song, Zhaoba Wang, Kailiang Xue, Youxing Chen, Guodong Guo, Maozhen Li, Asoke K. Nandi

“…Then we develop a non-local dual attention module to address the issue of severe feature loss in tiny defects. …”
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DTCformer: A Temporal Convolution-Enhanced Autoformer with DILATE Loss for Photovoltaic Power Forecasting by Quanhui Qiu, Dejun Ning, Qiang Guo, Jiang Wei, Huichang Chen, Lihui Sui, Yi Liu, Zibing Du, Shipeng Liu

“…The proposed model integrates a Temporal Convolution Feedforward Network module and a Variable Selection Embedding module, effectively capturing inter-variable dependencies and temporal periodicity. …”
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TF-CMFA: Robust Multimodal 3D Object Detection for Dynamic Environments Using Temporal Fusion and Cross-Modal Alignment by Yujing Wang, Abdul Hadi Abd Rahman, Fadilla 'Atyka Nor Rashid

“…However, most existing research seldom addresses the issues of robustness and performance degradation in dynamic environments due to the difficulty of aligning modal features. …”
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DAM-Faster RCNN: few-shot defect detection method for wood based on dual attention mechanism by Xingyu Tong, Zhihong Liang, Mingming Qin, Fangrong Liu, Jiayu Yang, Hengjiang Xiao, Wei Dai

“…To address the above issues, this paper proposes an improved Faster RCNN model based on a dual attention mechanism (DAM). …”
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Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features by Shenghua Xu, Zhuolu Wang, Jiping Liu, Xinrui Ma, Tingting Zhou, Qing Tang

“…To minimize computational costs, we design a depthwise separable residual module that optimizes traditional convolution on residual branches into depthwise separable convolution. …”
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S3FCD: a single-temporal self-supervised learning framework for remote sensing image change detection by Wenqian Lv, Nian Shi, Keming Chen, Guangyao Zhou, Chunlei Huo

“…To improve the quality of the generated image pairs, a deep feature-based generator (DFG) module is designed based on the pre-trained model. …”
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DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection by Weichao Pan, Jianmei Lei, Xu Wang, Chengze Lv, Gongrui Wang, Chong Li

“…DAPONet proposes three main innovations: (1) a dual attention mechanism that combines global context and local attention, (2) a multi-scale partial overparameterization module (CPDA), and (3) an efficient downsampling module (MCD). …”
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Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…Firstly, a multi-scale feature adaptive enhancement (MS-FAE) module is designed, integrating multi-level features and introducing a small object adaptive attention mechanism to enhance the representation ability for small objects. …”
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CHAM-CLAS: A Certificateless Aggregate Signature Scheme with Chameleon Hashing-Based Identity Authentication for VANETs by Ahmad Kabil, Heba Aslan, Marianne A. Azer, Mohamed Rasslan

“…Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). …”
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Remote Sensing Image Dehazing via Dual-View Knowledge Transfer by Lei Yang, Jianzhong Cao, He Bian, Rui Qu, Huinan Guo, Hailong Ning

“…The DVKT framework includes two novel knowledge-transfer modules: Intra-layer Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT) modules. …”
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Inversion of Magnetic Anomaly Based on Cross Attention Transformer by Juntao Lei, Jieru Chi, Shandong Li

“…However, existing deep learning methods for magnetic anomaly inversion suffer from issues such as the lack of accuracy in some model structures, poor boundary details, and the skin effect. …”
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GCML: Geometric Correlation Encoding Network With Multi-Scale Local Feature Extraction for Accurate Point Cloud Registration by Jinlei Zhuang, Ziteng Wang, Weiqiang Ma

“…This study introduces GCML, a novel detector-free approach that tackles these issues. For the first problem, GCML develops a geometric correlation encoding module (GCEM) that draws inspiration from the Denavit-Hartenberg (DH) modeling method in robotics to effectively encode the geometric correlations between each pair of points within point clouds. …”
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Anomaly detection in multidimensional time series for water injection pump operations based on LSTMA-AE and mechanism constraints by Mei Wang, Xinyuan Zhu, Guangyue Zhou, Kewen Li, Qingshan Wu, Wankai Fan

“…The LSTMA-AE framework encompasses three primary modules: a Time Feature Extraction Module (Encoder), an Attention Layer, and a Data Reconstruction Module (Decoder). …”
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