HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection by Ting Han, Chuanmu Li, Siyu Chen, Zongyue Wang, Jinhe Su, Yundong Wu, Guorong Cai

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Multi-scale conv-attention U-Net for medical image segmentation by Peng Pan, Chengxue Zhang, Jingbo Sun, Lina Guo

“…Abstract U-Net-based network structures are widely used in medical image segmentation. …”
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Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism by Zhichao Li, Gang Li, Ling Zhang, Guijuan Cheng, Shan Wu

“…Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions’ local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. …”
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CCTNet: CNN and Cross-Shaped Transformer Hybrid Network for Remote Sensing Image Semantic Segmentation by Honglin Wu, Zhaobin Zeng, Peng Huang, Xinyu Yu, Min Zhang

“…This model follows an encoder–decoder structure. It employs ResNet18 as an encoder to extract hierarchical feature information, and constructs a transformer decoder based on efficient cross-shaped self-attention to fully model local and global feature information and achieve lightweighting of the network. …”
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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…The model adopts a CNN-Transformer hierarchical structure, combining the strengths of Convolutional Neural Networks (CNNs) and Transformers to effectively capture and integrate both local and global features, thereby enhancing feature representation accuracy and diversity. …”
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Two-stream spatio-temporal GCN-transformer networks for skeleton-based action recognition by Dong Chen, Mingdong Chen, Peisong Wu, Mengtao Wu, Tao Zhang, Chuanqi Li

“…This study proposes a novel architecture addressing this limitation by implementing a parallel configuration of GCNs and the Transformer model (SA-TDGFormer). This parallel structure integrates the advantages of both the GCN model and the Transformer model, facilitating the extraction of both local and global spatio-temporal features, leading to more accurate motion information encoding and improved recognition performance. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

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CNN–Transformer gated fusion network for medical image super-resolution by Juanjuan Qin, Jian Xiong, Zhantu Liang

“…The network consists of two branches, one is the global branch based on residual Transformer network, and the other is the local branch based on dynamic convolutional neural network. …”
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VE-GCN: A Geography-Aware Approach for Polyline Simplification in Cartographic Generalization by Siqiong Chen, Anna Hu, Yongyang Xu, Haitao Wang, Zhong Xie

“…To enhance the graph convolutional structure for capturing crucial geographic element features and simultaneously learning vertex and edge features within map polylines, this study introduces a joint vertex–edge feature graph convolutional network (VE-GCN). …”
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Improving unsupervised pedestrian re‐identification with enhanced feature representation and robust clustering by Jiang Luo, Lingjun Liu

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MT-SCnet: multi-scale token divided and spatial-channel fusion transformer network for microscopic hyperspectral image segmentation by Xueying Cao, Hongmin Gao, Haoyan Zhang, Shuyu Fei, Peipei Xu, Peipei Xu, Zhijian Wang

“…IntroductionHybrid architectures based on convolutional neural networks and Transformers, effectively captures both the local details and the overall structural context of lesion tissues and cells, achieving highly competitive segmentation results in microscopic hyperspectral image (MHSI) segmentation tasks. …”
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MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images by Jiating Pan, Lishi Liang, Peng Sun, Yongbo Liang, Jianming Zhu, Zhencheng Chen

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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. …”
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A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. …”
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Multiscale Wavelet and Graph Network With Spectral Self-Attention for Hyperspectral Image Classification by Anyembe C. Shibwabo, Zou Bin, Tahir Arshad, Jorge Abraham Rios Suarez

“…Third, DH-GCN constructs a deep graph structure to model spatial topology and overcome oversmoothing. …”
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NAF-MEEF: A Nonlinear Activation-Free Network Based on Multi-Scale Edge Enhancement and Fusion for Railway Freight Car Image Denoising by Jiawei Chen, Jianhai Yue, Hang Zhou, Zhunqing Hu

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FD-YOLO11: A Feature-Enhanced Deep Learning Model for Steel Surface Defect Detection by Zichen Dang, Xingshuo Wang

“…To enhance the multiscale feature extraction process, self-calibrated convolution is integrated into the C3k2 module. Additionally, an FSPPF structure is designed to optimize the process of fusing local and global information, improving the defect recognition ability of the model in complex backgrounds. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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An OGFA+CNN Approach for Multi-Level Disease Identification in Fundus Images by Preethi Kulkarni, K. Srinivasa Reddy

“…Graph-based techniques are employed to capture the structural relationships between key elements such as blood vessels and the optic disc, providing valuable global context to the image. …”
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MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…To address the issue of lacking texture and structural information in the target images, we employ an integrated differential convolution (IDConv) module to extract richer image features during both the encoding and decoding stages. …”
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