FLE-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection by Xin Hu, Xiyu Wang, Yashu Chang, Jian Xiao, Hongliang Cheng, Firdaousse Abdelhad

“…Lastly, the Dyhead module is employed in the head section to unify multiple attention operations, improve the ability to resist interference from small objects and complex backgrounds. …”
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Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention by Dehui Li, Tao Liu, Ping Du, Yi He, Shuangtong Liu

“…Finally, the spatial SEM is introduced to enhance the feature pyramid network's ability to capture contextual information. The module consists of the large selective kernel network (LSKNet) and the SEM. …”
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YOLO-ALW: An Enhanced High-Precision Model for Chili Maturity Detection by Yi Wang, Cheng Ouyang, Hao Peng, Jingtao Deng, Lin Yang, Hailin Chen, Yahui Luo, Ping Jiang

“…Chili pepper, a widely cultivated and consumed crop, faces challenges in accurately determining maturity due to issues such as occlusion, small target size, and similarity between fruit color and background. …”
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A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention by Yang Yang, Ainuddin Wahid Bin Abdul Wahab, Norisma Binti Idris, Dingguo Yu, Chang Liu

“…The method integrates a self-supervised module into the initial image generation phase, leveraging attention mechanisms to enable autonomous mapping learning between image features. …”
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An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory by Yanbo Sun, Wenxing Bao, Wei Feng, Kewen Qu, Xuan Ma, Xiaowu Zhang

“…Secondly, a Global Atrous Spatial Pooling Pyramid Module (GASPPM) is proposed. Refined features at different depths and aggregated them to enhance the network’s ability to extract global semantics. …”
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MFFP-Net: Building Segmentation in Remote Sensing Images via Multi-Scale Feature Fusion and Foreground Perception Enhancement by Huajie Xu, Qiukai Huang, Haikun Liao, Ganxiao Nong, Wei Wei

“…This framework introduces three key innovations: (1) a Multi-Scale Feature Fusion (MFF) module that hierarchically aggregates shallow features through cross-level connections to enhance fine-grained detail preservation, (2) a Foreground Perception Enhancement (FPE) module that establishes pixel-wise affinity relationships within foreground regions to mitigate intra-class variance effects, and (3) a Dual-Path Attention (DPA) mechanism combining parallel global and local attention pathways to jointly capture structural details and long-range contextual dependencies. …”
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Multimodal Fusion Mamba Network for Joint Land Cover Classification Using Hyperspectral and LiDAR Data by Haizhu Pan, Ruixiang Zhao, Haimiao Ge, Moqi Liu, Quanxiu Zhang

“…Moreover, effectively integrating these heterogeneous features remains a significant difficulty. To address these issues, we propose the multimodal fusion Mamba network (M2FMNet), which consists of three key components: the spatial–spectral adaptive Mamba, the elevation-enhanced Mamba, and an enhanced fusion module. …”
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SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. …”
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A Novel Method for Traceability of Crude Oil Leakage on Offshore Platforms Based on Improved YOLOv5 Model Multi-Target Identification and Correlation Analysis by Zhenghua Wang, Shihai Zhang, Chongnian Qu, Zongyi Zhang, Feng Sun

“…In response to practical issues such as the complex structure of offshore platform scenes and the overlap of associated oil spill targets, a deformable convolution DCNv2 module is introduced to improve the model’s recognition accuracy for multi-shaped targets. …”
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SE-TransUNet-Based Semantic Segmentation for Water Leakage Detection in Tunnel Secondary Linings Amid Complex Visual Backgrounds by Renjie Song, Yimin Wu, Li Wan, Shuai Shao, Haiping Wu

“…An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was integrated into the UNet architecture, forming an SE-TransUNet model by incorporating SE-Block modules at skip connections between the encoder-decoder and the ViT output. …”
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Spatial-Channel Multiscale Transformer Network for Hyperspectral Unmixing by Haixin Sun, Qiuguang Cao, Fanlei Meng, Jingwen Xu, Mengdi Cheng

“…Specifically, a compact feature projection (CFP) module is first used to extract shallow discriminative features. …”
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RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion by Gang Liu, Yingzheng Huang, Shuguang Yan, Enxiang Hou

“…First, an efficient feature extraction module (EFEM) is created. It reconfigures the backbone network. …”
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Predicting the Imbalanced Impact of Drugs on Microbial Abundance Using Multi-View Learning and Data Augmentation by Bei Zhu, Haoyang Yu, Bingxue Du, Hui Yu, Jianyu Shi

“…It integrates features from both macro-view and micro-view to obtain more comprehensive representations, incorporates a data augmentation module to handle class imbalance, and uses a multilayer perceptron to predict the impact of drugs on microbial abundance. …”
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ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…First, we propose a Siamese neural network based on adaptive deformable convolution (ADC) modules. The ADC module incorporates spatial offset parameters into convolutional kernel sampling and mapping weights to capture irregularly varying edge features for local adaptive receptive fields. …”
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Small Object Detection in UAV Remote Sensing Images Based on Intra-Group Multi-Scale Fusion Attention and Adaptive Weighted Feature Fusion Mechanism by Zhe Yuan, Jianglei Gong, Baolong Guo, Chao Wang, Nannan Liao, Jiawei Song, Qiming Wu

“…In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection model named IA-YOLOv8 in this paper. …”
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A multi-scale remote sensing semantic segmentation model with boundary enhancement based on UNetFormer by JiangQing Wang, Ting Chen, Lu Zheng, Jun Tie, YiBo Zhang, PinTing Chen, ZhiQing Luo, QuanJie Song

“…In this work, to address these issues, a Boundary-Enhanced Multi-Scale Semantic Segmentation Network (BEMS-UNetFormer) based on UNetFormer is proposed for remote sensing data. …”
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MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation by Dejie Chen, Xiangping Chen, Hao Gu, Su Zhao, Hao Jiang

“…The proposed framework enhances performance through two innovations: 1) replacement of standard encoder blocks with a multi-branch module combining heterogeneous convolutions to achieve multi-scale receptive field diversification; 2) redesign of skip connections through a pyramid fusion module with spatial attention for adaptive multi-level feature weighting. …”
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Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm by Xiulin Qiu, Haozhi Zhang, Chang Yuan, Qinghua Liu, Hongzhi Yao

“…Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. …”
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Enhancing Underwater Video from Consecutive Frames While Preserving Temporal Consistency by Kai Hu, Yuancheng Meng, Zichen Liao, Lei Tang, Xiaoling Ye

“…In addition, to address the limitations of traditional U-Net models in handling complex multiscale feature fusion, this study proposes a novel underwater feature fusion module. By applying both max pooling and average pooling, this module separately extracts local and global features. …”
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CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning by Yun Chu, Qiuhao Wang, Enze Zhou, Ling Fu, Qian Liu, Gang Zheng

“…Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. …”
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