AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation by Jie Li, Changchun Yang

“…However, most existing models fail to distinguish the importance of different nodes, which limits their performance. To address this issue, we propose the asGCL model. To mitigate the prevalent issue of popularity bias and to learn more uniform embedding representations, we have integrated a lightweight contrastive learning module into our model. …”
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METHODOLOGICAL ASPECTS AND RESULTS OF CONDUCTING FOCUS GROUP INTERVIEWS IN THE STUDY OF THE OPINION OF CHILDREN LEFT BEHIND BY LABOR MIGRATION by Elena VACULOVSCHI

“…Each module, the experience of conducting focus group interviews and the results of the opinion study of children left behind by labor force migration were presented and analyzed in depth in the article.…”
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Improved image reconstruction from brain activity through automatic image captioning by Fatemeh Kalantari, Karim Faez, Hamidreza Amindavar, Soheila Nazari

“…Our proposed method consists of two main modules: visual reconstruction and semantic reconstruction. …”
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EML-SlowFast: A behavior recognition model for lion-head goose by Jinwei Wang, Zhiguo Du, Bin Wen, Zhihui Wu, Xudong Lin

“…The Efficient Channel Attention Bottleneck (ECAbneck) module and the Large Kernel Global-Local Feature Extraction (LGLE) module are designed and incorporated into the model. …”
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MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation by Ruiyuan Chen, Saiqi He, Junjie Xie, Tao Wang, Yingying Xu, Jiangxiong Fang, Xiaoming Zhao, Shiqing Zhang, Guoyu Wang, Hongsheng Lu, Zhaohui Yang

“…For feature fusion and enhancement, the designed hybrid attention mechanisms combine four different attention modules: (1) an atrous spatial pyramid pooling (ASPP) module for the CNN branch, (2) a cross attention module in the encoder for fusing local and global features, (3) an adaptive cross attention (ACA) module in skip connections for further performing fusion, and (4) a squeeze-and-excitation attention (SE-attention) module in the decoder for highlighting informative features. …”
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YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection by Yuxi Huang, Hong Zhao, Jie Wang

“…Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. …”
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A Vehicle–Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion by Jianhu Liu, Ping Wang, Xia Wu

“…For feature fusion at each scale, we introduce the Multi-Source Dynamic Interaction Module (MSDI) and the Per-Point Self-Attention Module (PPSA). …”
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SP-IGAN: An Improved GAN Framework for Effective Utilization of Semantic Priors in Real-World Image Super-Resolution by Meng Wang, Zhengnan Li, Haipeng Liu, Zhaoyu Chen, Kewei Cai

“…This information is injected into the RRDB module through Spatial Feature Transform (SFT) layers, generating more accurate and semantically consistent texture details. …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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SOC Equalization Control Method Considering SOH in DC–DC Converter Cascaded Energy Storage Systems by Shixian Bai, Xiangqian Tong, Xin Ma, Jie Zhou

“…The “barrel effect” diminishes the effective capacity of the energy storage system. To mitigate this issue, a DC–DC converter cascaded energy storage system has been developed, incorporating precise charge and discharge management for each battery module within a cluster. …”
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Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation by Zhanlong Chen, Rui Wang, Yongyang Xu

“…Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. …”
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Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet by Wenhao Zeng, Gongbing Su, Zixuan Su, Rui Li, Jun Chen

“…The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. …”
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Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition by Xin Deng, Xinyi Hong, LiJiao Ai, Xingchen Li, Chenhui Li

“…The MSRSDA model comprises three components: a data augmentation module, a data selector based on the actor-critic framework, and a domain adaptation module. …”
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AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network by Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji

“…Then, we propose a feature fusion weight generation module (FFWG), which can adaptively generate fusion weights based on the above aggregated node features. …”
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TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

“…Finally, a band evaluation (BE) module is employed to assess the band selection results and optimize the model parameters. …”
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Mixed multi-branch feature fusion model for efficient automatic building extraction from high-resolution remote sensing images by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Taoyi Chen, Hu Jiang, Fei Su

“…Firstly, we designed a Mixed Multi-Branch Feature Fusion (MMFF) module, which performs multi-dimensional weighted fusion on the feature information captured by the Transformer. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…To further enhance detection performance, a dynamic depthwise separable convolution is designed in the CTIM module, which can adaptively adjust convolution kernels to more precisely capture change features in different regions of the image. …”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…DiffMamba uses a hybrid CNNs-Transformer as the encoder structure, and is equipped with the efficient phase sensing module (EPSM), the multi-view transformer module (MVTrans), the semantic diffusion alignment module (SDAM), and the coordinate state space model (CAMamba). …”
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TSAS—YOLOv8: An Optimization Detection Model for Capturing Small Target Features and Processing Key Information by Yongbo Yuan, Linlin Cao

“…This not only reduces the detection accuracy but also undermines the model’s generalization performance. To address this issue, we propose the TSAS-YOLOv8 method. This method defines the T-CMUNeXt module and introduces a new Backbone structure based on this module, leveraging its multi-scale feature fusion advantage to capture the subtle features of small targets. …”
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A One-Stage HMDV Algorithm Applied in Multitarget Detection in SAR Images by Lei Pang, Weihe Huang, Fengli Zhang, Yinhong Song

“…First, a hybrid feature extraction module is designed to address the computational complexity caused by increased width and depth in convolutional neural networks. …”
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