VM-UNet++ research on crack image segmentation based on improved VM-UNet by Wenliang Tang, Ziyi Wu, Wei Wang, Youqin Pan, Weihua Gan

“…Abstract Cracks are common defects in physical structures, and if not detected and addressed in a timely manner, they can pose a severe threat to the overall safety of the structure. …”
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EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation by Zhe Yang, Baozhong Mu, Mingxun Wang, Xin Wang, Jie Xu, Baolu Yang, Cheng Yang, Hong Li, Rongqi Lv

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MSDCA: A Multi-Scale Dual-Branch Network with Enhanced Cross-Attention for Hyperspectral Image Classification by Ning Jiang, Shengling Geng, Yuhui Zheng, Le Sun

“…First, a multiscale 3D spatial–spectral feature extraction module (3D-SSF) employs parallel 3D convolutional branches with diverse kernel sizes and dilation rates, enabling hierarchical modeling of spatial–spectral representations from large-scale patches and effectively capturing both fine-grained textures and global context. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Recently, graph-based methods have also been used to predict trajectories, however processing graph-structured data introduces significant increase in computation. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
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A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes by Zhi Yang, Changzheng Liu, Ping Mei, Lei Wang

“…During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. …”
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Enhanced Image Retrieval Using Multiscale Deep Feature Fusion in Supervised Hashing by Amina Belalia, Kamel Belloulata, Adil Redaoui

“…By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. …”
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Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion by Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao

“…The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. …”
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HTCNN-Attn: a fine-grained hierarchical multi-label deep learning model for disaster emergency information intelligent extraction from social media by Shanshan Li, Qingjie Liu, Xiaoling Sun

“…It integrates a three-level tree-structured labeling architecture, Transformer-based global feature extraction, convolutional neural network (CNN) layers for local pattern capture, and a hierarchical attention mechanism. …”
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Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Finally, a multi-layer graph convolutional network with a hierarchical attention mechanism was designed, in which local and global features were hierarchically extracted and fused through a multi-level neighborhood aggregation strategy, significantly enhancing the model’s capability to identify key features. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

“…This approach incorporates image texture features with enhanced Local Binary Pattern (LBP), providing insights into the local structure and layout of images and aiding the model in better understanding image details and internal structure, thus enhancing classification performance. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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UNestFormer: Enhancing Decoders and Skip Connections With Nested Transformers for Medical Image Segmentation by Adnan Md Tayeb, Tae-Hyong Kim

“…Precise identification of organs and lesions in medical images is essential for accurate disease diagnosis and analysis of organ structures. Deep convolutional neural network (CNN)-based U-shaped networks are among the most popular and promising approaches for this task. …”
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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…The method combines the structures of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to extract spatial and temporal features from digital library network security data. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…In this study, four deep convolutional neural network models were designed with Occam's razor principle through hyperparameter settings on the algorithm structure aspect in the form of number of layers and optimization aspect in the form of optimizer type. …”
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A Drug-Target Interaction Prediction Method Based on Attention Perception and Modality Fusion by PENG Yang, ZHU Xiaofei, HU Dongdong

“…[Methods] For drug branches, Graph Transformer and Graph Convolutional Neural Network were used to jointly characterize the global structures and biochemical information of drug molecules. …”
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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

“…Notably, we adopt the Covariance Matrix Adaptation Evolution Strategy and global adaptive pruning technique to realize automated network structure design, which further improves the model training efficiency and automation, and avoids the uncertainty problem of network design. …”
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HDF-Net: Hierarchical Dual-Branch Feature Extraction Fusion Network for Infrared and Visible Image Fusion by Yanghang Zhu, Mingsheng Huang, Yaohua Zhu, Jingyu Jiang, Yong Zhang

“…Remarkably, we propose a pin-wheel-convolutional transformer (PCT) module that integrates local convolutional processing with directional attention to improve low-frequency feature extraction, thereby enabling more robust global–local context modeling. …”
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