DSS-MobileNetV3: An Efficient Dynamic-State-Space- Enhanced Network for Concrete Crack Segmentation by Haibo Li, Yong Cheng, Qian Zhang, Lingkun Chen

“…The DSS-MobileNetV3 adopts a U-shaped encoder–decoder architecture, and a dynamic-state-space (DSS) block is designed into the encoder to improve the MobileNetV3 bottleneck module in modeling global dependencies. The DSS block improves the MobileNetV3 model in structural perception and global dependency modeling for complex crack morphologies by integrating dynamic snake convolution and a state space model. …”
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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. …”
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Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…However, due to the fact that these models have sequential network structures and are unable to simultaneously focus on local and global features, thus, resulting in a reduction in recognition performance. …”
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AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder by Yunseung Lee, Pilsung Kang

“…These methods are actively used in various fields such as manufacturing, medical care, and intelligent information. Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. …”
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RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset. by Cheng Xing, Ronald Xie, Gary D Bader

“…We developed the RETINA method, which combines pre-training on the large, unlabeled CEM500K EM image dataset with a hybrid neural-network model architecture that integrates both local (convolutional layer) and global (transformer layer) image processing to learn from manual image annotations. …”
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DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation by Elizar Elizar, Rusdha Muharar, Mohd Asyraf Zulkifley

“…By processing features at different spatial resolutions, the multiscale densely connected layer in the form of the Pyramid Pooling Dense Module (PPDM) helps the network to capture both local and global context, preserving finer details of the cardiac structure while also capturing the broader context required to accurately segment larger cardiac structures. …”
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A New Hybrid ConvViT Model for Dangerous Farm Insect Detection by Anil Utku, Mahmut Kaya, Yavuz Canbay

“…This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. …”
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Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images by Feifeng Liu, Weihu Wang

“…The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. …”
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TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening by Zeinab Dehghan, Jingxiang Yang, Mehran Yazdi, Abdolraheem Khader, Liang Xiao

“…In this study, we focus on leveraging the power of CNN and transformer models and propose a multistage deep transformer-based super-resolution network that is regularized via an asymmetric autoencoder structure. In addition, we utilize a 3-D convolution layer in the light transformer structure because it allows for more flexible computation of correlations between HSI layers and better capturing of dependencies within spectral–spatial features. …”
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Attention residual network for medical ultrasound image segmentation by Honghua Liu, Peiqin Zhang, Jiamin Hu, Yini Huang, Shanshan Zuo, Lu Li, Mailan Liu, Chang She

“…Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. …”
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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Furthermore, the graph structure is dynamically updated using a weighted summation approach.Experiments demonstrate that the proposed method significantly improves prediction accuracy on the PeMSD4 and PeMSD8 datasets. …”
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Generation driven understanding of localized 3D scenes with 3D diffusion model by Hao Sun, Junping Qin, Zheng Liu, Xinglong Jia, Kai Yan, Lei Wang, Zhiqiang Liu, Shaofei Gong

“…However, the existing diffusion models primarily focus on the global structure and are constrained by predefined dataset categories, which are unable to accurately resolve the detailed structure of complex 3D scenes. …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…A unique combination of convolution and pooling operations was used by the CPCA attention mechanism to perform global average pooling and maximum pooling on the input feature map, multi-dimensional feature information was deeply mined, and then attention weights for each channel and spatial position were accurately generated through nonlinear transformation, guiding the model to focus on the key feature areas of foreign objects and enhance feature extraction capabilities. …”
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Graph-Based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing by Hua Dong, Xiaohua Zhang, Jinhua Zhang, Hongyun Meng, Licheng Jiao

“…In the method, HSIs are treated as data on manifold structures, with superpixels serving as graph nodes to construct a global graph-structured data. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…Existing deep learning models typically adopt encoder-decoder architectures integrating convolutional layers with global dependency modeling to capture broader contextual information around tumors. …”
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HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation by Soumya Sara Koshy, L. Jani Anbarasi

“…The model achieved a Jaccard index of 98.04% and 94.84% and a Dice similarity coefficient of 99.01% and 97.35% on the BUSI and BrEaST datasets, respectively.DiscussionThe ConvMixer and ConvNeXT modules are integrated with convolution-enhanced multihead attention, which enhances the model's ability to capture local and global contextual information. …”
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Diagnosis of Alzheimer’s disease using brain $$^{18}\textrm{F}$$ -FDG PET imaging based on a state space model by Yufang Dong, Yonglin Chen, Zhe Jin, Xingbo Dong

“…Building on this, we optimized the original purely convolutional structure into a hybrid architecture combining convolution and Transformer layers. …”
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