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  1. 1621

    Weakly Supervised Semantic Segmentation of Remote Sensing Images Using Siamese Affinity Network by Zheng Chen, Yuheng Lian, Jing Bai, Jingsen Zhang, Zhu Xiao, Biao Hou

    Published 2025-02-01
    “…First, we design a seed enhancement module for semantic affinity, which strengthens contextual relevance in the feature map by enforcing a unified constraint principle of cross-pixel similarity, thereby capturing semantically similar regions within the image. …”
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  2. 1622

    A YOLO11-Based Method for Segmenting Secondary Phases in Cu-Fe Alloy Microstructures by Qingxiu Jing, Ruiyang Wu, Zhicong Zhang, Yong Li, Qiqi Chang, Weihui Liu, Xiaodong Huang

    Published 2025-07-01
    “…Specifically, the EIEM module enhances the C3K2 structure to improve edge perception; the CSPSA module is optimized into C2CGA to strengthen multi-scale feature representation; and the RepGFPN and DySample techniques are integrated to construct the GDFPN neck network. …”
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  3. 1623

    Hyperspectral Image Super-Resolution via Grouped Second-Order Spatial Features and Spectral Attention Network by Allen Patnaik, M. K. Bhuyan, Sultan Alfarhood, Mejdl Safran

    Published 2025-01-01
    “…We employed a grouped spectral attention (GSA) module to extract spectral details across different bands of HSI images. …”
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  4. 1624

    RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos, Yansong Bao

    Published 2024-12-01
    “…To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. …”
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    Article
  5. 1625

    SFG-Net: A Scattering Feature Guidance Network for Oriented Aircraft Detection in SAR Images by Qingyang Ke, Youming Wu, Wenchao Zhao, Qingbiao Meng, Tian Miao, Xin Gao

    Published 2025-03-01
    “…The core components of the proposed method include the detail feature supplement (DFS) module and the context-aware scattering feature enhancement (CAFE) module. …”
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  6. 1626

    Wheat disease recognition method based on the SC-ConvNeXt network model by Tianliang Dong, Xiao Ma, Bin Huang, Wenyu Zhong, Qingan Han, Qinghai Wu, You Tang

    Published 2024-12-01
    “…Subsequently, the CBAM attention module is integrated into ConvNeXt-T to enhance the model’s feature extraction and generalization capabilities in complex backgrounds, and each attention module’s loss function is improved with a LeakyReLU activation function to prevent neuron deactivation when inputs are negative. …”
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    Article
  7. 1627

    PigFRIS: A Three-Stage Pipeline for Fence Occlusion Segmentation, GAN-Based Pig Face Inpainting, and Efficient Pig Face Recognition by Ruihan Ma, Seyeon Chung, Sangcheol Kim, Hyongsuk Kim

    Published 2025-03-01
    “…PigFRIS employs state-of-the-art occlusion detection with the YOLOv11 segmentation model, a GAN-based inpainting reconstruction module using AOT-GAN, and a lightweight recognition module tailored for pig face classification. …”
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    Article
  8. 1628

    ELTrack: Events-Language Description for Visual Object Tracking by Mohamad Alansari, Khaled Alnuaimi, Sara Alansari, Sajid Javed

    Published 2025-01-01
    “…Additionally, we generate NL descriptions using a Visual-Language (VL) image-captioning module featuring BLIP-2 and GPT-4. These modalities are seamlessly integrated using a superimpose fusion module to enhance tracking performance. …”
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    Article
  9. 1629

    Principles of Organization and Functioning of Mini-CHP Plants Using Local Fuels in Conditions of Hydrogen Energy by R. S. Ignatovich

    Published 2025-02-01
    “…The analysis of literary sources on the current state of development of mini-CHP plants on LF in the structure of the country's energy balance revealed a number of issues related to their operation and construction that arose after the commissioning of the Belarusian NPP. …”
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    Article
  10. 1630
  11. 1631

    DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles by Siqiang Cheng, Lingshan Chen, Kun Yang

    Published 2025-06-01
    “…The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. …”
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  12. 1632

    Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling by Jiahui Liu, Lili Zhang, Xianjun Wang

    Published 2025-07-01
    “…It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. …”
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  13. 1633

    ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks by Xiao Hu, Qihao Chen, Xiuguo Liu, Gang Deng, Cheng Chi, Bin Wang

    Published 2025-03-01
    “…However, challenges persist due to the presence of numerous noisy pixels in the image background and the diverse and intricate morphologies of cracks, leading to issues such as misclassification and omission. To address these issues, this paper proposes a refined pixel-level segmentation network (ANF-Net) suitable for complex crack detection scenarios with high noise levels and diverse crack morphologies. …”
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    Article
  14. 1634

    Reliability, validity, and acceptability of the simplified Mandarin Chinese EORTC QLQ-OPT30 for uveal melanoma patients by Yonghui Huang, Panpan Cui, Guangyan Dong, Tongfang Fan

    Published 2025-07-01
    “…The module measures various aspects such as ocular irritation, visual impairment, headaches, concerns about disease recurrence, appearance issues, functional problems related to vision impairment, reading difficulties, functional issues in the treated eye, and driving problems. …”
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    Article
  15. 1635

    Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes by Fukai Zhang, Shuo Zhao, Haiyan Zhang, Yongqiang Ma, Qiang Zhang, Shaopu Wang, Wenjing Chang

    Published 2025-09-01
    “…DrillNet comprises two main components: the YOLO with Multi-Scale Global Context Aggregation Network (YOLO-GC) and the Drill-Count module.The core architecture of YOLO-GC integrates the WaveletPool module, C2f-EMSCP feature extraction unit, GCFPN global context fusion pyramid network, and the oriented bounding box detection head (OBBHead), thereby effectively tackling the issues of insufficient detection accuracy and robustness in challenging coal mine scenarios. …”
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  16. 1636

    Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) by Shadman Darvishi, Karim Solaimani

    Published 2020-12-01
    “…Kermanshah city as one of the growing areas in recent years has experienced a large population growth and due to the role of population in land use changes and vegetation cover, this issue requires awareness of the vegetation status of this area for proper management of natural resources. …”
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  17. 1637
  18. 1638

    Large-Scale Apple Orchard Identification from Multi-Temporal Sentinel-2 Imagery by Chunxiao Wu, Yundan Liu, Jianyu Yang, Anjin Dai, Han Zhou, Kaixuan Tang, Yuxuan Zhang, Ruxin Wang, Binchuan Wei, Yifan Wang

    Published 2025-06-01
    “…Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. …”
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    Article
  19. 1639

    RSNC-YOLO: A Deep-Learning-Based Method for Automatic Fine-Grained Tuna Recognition in Complex Environments by Wenjie Xu, Hui Fang, Shengchi Yu, Shenglong Yang, Haodong Yang, Yujia Xie, Yang Dai

    Published 2024-11-01
    “…Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. …”
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  20. 1640

    Education students in Zambia – comparative education in action by Dan Davies

    Published 2008-12-01
    “…They were self-funding and participating in a second year undergraduate module entitled ‘Education in Africa’ which examines generic educational and development issues through case studies in different African countries. …”
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