GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Current methods struggle to simultaneously preserve global structure, model cellular dynamics, and handle technical noise effectively. …”
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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. …”
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Cross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation by Pengnan Liu, Yanchen Wang, Yunlong Li, Deqiang Cheng, Feixiang Xu

“…Secondly, a multi-branch feature fusion structure is constructed. By fusing different feature information from the global and occlusion branches, the diversity of features is enriched. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
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Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy by Renyuan Liu, Yunyu Shi, Xian Tang, Xiang Liu

“…In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. …”
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Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification by Ming Sun, Jie Zhang, Xiaoou He, Yihe Zhong

“…The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. …”
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SwinNowcast: A Swin Transformer-Based Model for Radar-Based Precipitation Nowcasting by Zhuang Li, Zhenyu Lu, Yizhe Li, Xuan Liu

“…Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…A bidirectional fusion module (BFM) is then designed to comprehensively integrate spatial details and global information, thereby enabling accurate identification of boundaries between adjacent buildings, and maintaining the structural integrity of buildings to avoid internal holes. …”
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DASNet a dual branch multi level attention sheep counting network by Yini Chen, Ronghua Gao, Qifeng Li, Hongtao Zhao, Rong Wang, Luyu Ding, Xuwen Li

“…DASNet is built on a modified VGG–19 architecture, where a dual-branch structure is employed to integrate both shallow and deep features. …”
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MFPI-Net: A Multi-Scale Feature Perception and Interaction Network for Semantic Segmentation of Urban Remote Sensing Images by Xiaofei Song, Mingju Chen, Jie Rao, Yangming Luo, Zhihao Lin, Xingyue Zhang, Senyuan Li, Xiao Hu

“…The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. …”
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DRDA-Net: Deep Residual Dual-Attention Network with Multi-Scale Approach for Enhancing Liver and Tumor Segmentation from CT Images by Wail M. Idress, Yuqian Zhao, Khalid A. Abouda, Shaodi Yang

“…Additionally, we introduce a unique pre-processing pipeline employing a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. …”
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Applying SSVEP BCI on Dynamic Background by Junkai Li, Boxun Fu, Fu Li, Wenkai Gu, Youshuo Ji, Yang Li, Tiejun Liu, Guangming Shi

“…MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. …”
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A method for identifying gully-type debris flows based on adaptive multi-scale feature extraction by Qiuyu Liu, Ting Wang, Zhijie Zheng, Baoyun Wang

“…First, the feature extraction component consists of a dual-branch structure with a global feature extraction part based on self-attention mechanisms and a local feature extraction part based on multi-scale methods, designed to extract gully features at different scales and establish connections among them. …”
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A secured accreditation and equivalency certification using Merkle mountain range and transformer based deep learning model for the education ecosystem by Sumathy Krishnan, Surendran Rajendran, Mohammad Zakariah

“…TCRN employs Bi-GRU to retain long-term academic trends, Depth-wise separable convolutions (DSC) to concentrate on course-specific information, and BERT to capture global semantic context. …”
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Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism by Qi Chen, Feng Zhang, Yin Wang, Qing Yu, Genfeng Lang, Lixiong Zeng

“…Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. …”
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A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells by Tianqi Chen, Yuki Todo, Zhiyu Qiu, Yuxiao Hua, Hiroki Sugiura, Zheng Tang

“…Additionally, in contrast to previous artificial visual systems (AVSs), our findings suggest that lateral geniculate nucleus (LGN) structures, though present in biological vision, may not be essential for motion direction detection. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…In this context, nations have accelerated the transition of their energy structures to reduce dependence on fossil fuels and lower carbon emissions. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. …”
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Extensive identification of landslide boundaries using remote sensing images and deep learning method by Chang-dong Li, Peng-fei Feng, Xi-hui Jiang, Shuang Zhang, Jie Meng, Bing-chen Li

“…SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. …”
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