An Omni-Dimensional Dynamic Convolutional Network for Single-Image Super-Resolution Tasks by Xi Chen, Ziang Wu, Weiping Zhang, Tingting Bi, Chunwei Tian

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HSI Reconstruction: A Spectral Transformer With Tensor Decomposition and Dynamic Convolution by Le Sun, Xihan Ma, Xinyu Wang, Qiao Chen, Zebin Wu

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LO-MLPRNN: A Classification Algorithm for Multispectral Remote Sensing Images by Fusing Selective Convolution by Xiangsuo Fan, Yan Zhang, Yong Peng, Qi Li, Xianqiang Wei, Jiabin Wang, Fadong Zou

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Small object detection algorithm based on improved YOLOv10 for traffic sign by Yukang Zou, Scarlett Liu

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YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases by Tianyue Jiang, Xu Du, Ning Zhang, Xiuhan Sun, Xiao Li, Siqing Tian, Qiuyan Liang

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Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features by Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang

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Dense dynamic convolutional network for Bel canto vocal technique assessment by Zhenyi Hou, Xu Zhao, Shanggerile Jiang, Daijun Luo, Xinyu Sheng, Kaili Geng, Kejie Ye, Jiajing Xia, Yitao Zhang, Chenxi Ban, Jiaxing Chen, Yan Zou, Yuchao Feng, Xin Yuan, Guangyu Fan

“…To address the challenges posed by complex spectral features and meet the demands for objective vocal technique assessment, we introduce Omni-Dimensional Dynamic Convolution (ODConv). Additionally, we employ densely connected layers to optimize the framework, enabling efficient utilization of multi-scale features across multiple dynamic convolution layers. …”
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A Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression by Q. Z. Fang, S. B. Gu, J. G. Wang, L. L. Zhang

“…Deep learning-based remote sensing image compression methods show great potential, but traditional convolutional networks mainly focus on local feature extraction and show obvious limitations in dynamic feature learning and global context modeling. …”
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Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA by WU Xinzhong, LUO Kang, TANG Shoufeng, HE Zexu, CHEN Qi

“…Built upon ConvNeXt-T, Batch Normalization (BN) technology was introduced to improve the network's generalization ability. Omni-dimensional Dynamic Convolution (ODConv) replaced the original depthwise separable convolution to enhance the adaptability of the network. …”
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Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang, Jinrong Duan

“…In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. …”
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ILViT: An Inception-Linear Attention-Based Lightweight Vision Transformer for Microscopic Cell Classification by Zhangda Liu, Panpan Wu, Ziping Zhao, Hengyong Yu

“…COKA integrates Omni-Dimensional Dynamic Convolution (ODC), linear attention, and a Kolmogorov-Arnold Network(KAN) structure to enhance feature learning and model interpretability. …”
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TDR-Model: Tomato Disease Recognition Based on Image Dehazing and Improved MobileNetV3 Model by Zhixiang Zhang, Tong Liu, Jinyu Gao, Meng Yang, Wenjun Luo, Fanqiang Lin

“…Moreover, MobileNetV3 is improved by the incorporation of the convolutional block attention module (CBAM) and Omni-Dimensional Dynamic Convolution(ODC), improving the accuracy of disease feature recognition. …”
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Exposed conductor detection of 10 kV distribution line based on improved YOLOv8 by Qiwen JING, Sipeng HAO, Siyuan LI

“…The algorithm replaces the original convolution with omni-dimensional dynamic convolution in the backbone network, enhancing the features of exposed conductors through multi-dimensional feature extraction. …”
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Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery by Bojie Chen, Qianran Zhang, Na Yang, Xiukun Wang, Xiaobo Zhang, Yilan Chen, Shengli Wang

“…The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. …”
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ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao, Wenfeng Li

“…First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. …”
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Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network by Zehua Li, Zehua Li, Yihui Pan, Xu Ma, Yongjun Lin, Xicheng Wang, Hongwei Li

“…The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. …”
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Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery by Shaoxiong Xu, Wenjiang Huang, Dacheng Wang, Biyao Zhang, Hong Sun, Jiayu Yan, Jianli Ding, Jinjie Wang, Qiuli Yang, Tiecheng Huang, Xu Ma, Longlong Zhao, Zhuoqun Du

“…The improved YOLOv8 model used omni-dimensional dynamic convolution (ODConv) to enhance the performance of convolutional networks, designed a dynamic head (DyHead) module to capture PWD features more accurately, and applied MPDioU to improve the regression accuracy and model runtime efficiency. …”
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A Universal Tire Detection Method Based on Improved YOLOv8 by Chi Guo, Mingxia Chen, Junjie Wu, Haipeng Hu, Luobing Huang, Junjie Li

“…Subsequently, four network structures were created in response to the need for lightweighting the detection procedure by incorporating the omni-dimensional dynamic convolution (ODConv) network structure at various positions. …”
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