Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion by Yuanyuan Xu, Zhi Wang, Xiaochang Liu

“…During the feature extraction process, the SENet module is first introduced to enhance the ability to extract distinctive features. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…Our model learns dual-path selection signals via a primary module and introduces two auxiliary modules to adjust these signals. …”
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YOLOv10-LGDA: An Improved Algorithm for Defect Detection in Citrus Fruits Across Diverse Backgrounds by Lun Wang, Rong Ye, Youqing Chen, Tong Li

“…Furthermore, we integrate the AFPN module to enhance the model’s detection capability for targets of varying scales. …”
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Complementary Local–Global Optimization for Few-Shot Object Detection in Remote Sensing by Yutong Zhang, Xin Lyu, Xin Li, Siqi Zhou, Yiwei Fang, Chenlong Ding, Shengkai Gao, Jiale Chen

“…Specifically, we design an Extensible Local Feature Aggregator Module (ELFAM) that reconstructs object structures via multi-scale recursive attention aggregation. …”
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Downhole Coal–Rock Recognition Based on Joint Migration and Enhanced Multidimensional Full-Scale Visual Features by Bin Jiao, Chuanmeng Sun, Sichao Qin, Wenbo Wang, Yu Wang, Zhibo Wu, Yong Li, Dawei Shen

“…Additionally, a multi-scale luminance adjustment module is integrated to merge features across perceptual ranges, mitigating localized brightness anomalies such as overexposure. …”
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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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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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A dense multi-pooling convolutional network for driving fatigue detection by Qing Han, Shimiao Cui, Weidong Min, Cong Yan, Li Liu, Feng Ning, Longfei Li

“…., when wearing glasses or in the presence of non-driver individuals). To address these issues, this paper proposes a driving fatigue detection method based on a novel network and the analysis of driver facial actions. …”
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DBDB: de-bimodal defocus blur in joint infrared-visible imaging by Zhe Cao, Lixin Xu, Jin Zhang, Biwen Yang, Kaizheng Chen, Ruiheng Zhang

“…In the latter case, the relative nature of the blur effect can lead to ambiguity in determining which modality’s information should be prioritized for guidance, and conflicts may arise between the clear components of the blurred image and the blurry components of the clear image. To address these issues, we propose the first de-bimodal defocus blur (DBDB) method, which consists of a low-frequency semantic hold (LSH) module with a pre-trained infrared model and a cross-modal complementary feature induction (CCFI) module driven by a max-min blur entropy loss. …”
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ES-Net Empowers Forest Disturbance Monitoring: Edge–Semantic Collaborative Network for Canopy Gap Mapping by Yutong Wang, Zhang Zhang, Jisheng Xia, Fei Zhao, Pinliang Dong

“…Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. …”
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YOLOv10-CBRC: A high-precision document image layout analysis model by Zhenjie Wu, Weilan Wang, Hongrui Li

“…However, two significant issues persist: (1) the inadequacy of Tibetan document datasets;(2) insufficient utilization of layout information by existing models. …”
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MSFNet3D: Monocular 3D Object Detection via Dual-Branch Depth-Consistent Fusion and Semantic-Guided Point Cloud Refinement by Rong Yang, Zhijie You, Renhui Luo

“…However, existing pseudo-lidar methods encounter challenges such as coarse quality and insufficient semantic information when generating 3D point clouds from monocular images. To address these issues, this paper introduces MSFNet3D, which aims to overcome the quality limitations of pseudo-lidar point cloud. …”
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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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Fine-Grained Style Alignment and Class Balance for Unsupervised Domain Adaptation in Remote Sensing Image Segmentation by Yousheng Xu, Weiji Wang, Wei Yao, Shengzhou Xu

“…In addition, class imbalance causes the model to be biased toward dominant classes, resulting in decreased overall classification accuracy. To address these issues, this article proposes two innovative modules. …”
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Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments by Yi Wang, Xiang Li

“…First, we introduce the high-low frequency (HiLo) attention into the intra-scale feature interaction module to enhance the extraction of both high- and low-frequency features, reduce computational complexity, and improve detection performance. …”
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A construction of heterogeneous transfer learning model based on associative fusion of image feature data by Wen-Fei Tian, Ming Chen, Zhong Shu, Xue-jun Tian

“…When heterogeneous transfer learning is applied some difficulties and further issues appear. For example, noise in image recognition appears and is required to be reduced. …”
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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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