PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…PIC2O-Sim features a causality-aware dynamic convolutional neural operator as its backbone model that honors the space–time causality constraints via careful receptive field configuration and explicitly captures the permittivity-dependent light propagation behavior via an efficient dynamic convolution operator. …”
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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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Vision-Based American Sign Language Classification Approach via Deep Learning by Nelly Elsayed

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A COMPARATIVE ANALYSIS OF DEEP TRANSFER LEARNING TECHNIQUES FOR MAMMOGRAPHIC IMAGE CLASSIFICATION by Bhavesh Gupta, Akshay Singh, Anjana Gosain

“…The pre-trained convolution neural network models of VGG-16, VGG-19, ResNet50 and Inception V3 are worked as Deep Transfer Learning on two databases: the Mammography Image Analysis Society (MIAS) database containing 321 images, and Chinese Mammography Database (CMMD) containing 3744 mammography, of which 2000 images are used for learning. …”
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A colonic polyps detection algorithm based on an improved YOLOv5s by Jianjun Li, Jinhui Zhao, Yifan Wang, Jinhui Zhu, Yanhong Wei, Junjiang Zhu, Xiaolu Li, Shubin Yan, Qichun Zhang

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A damage assessment method based on recent-time acoustic emission events for composite interlaminar damage by Jingye Hao, Su Ju, Ke Duan, Pengcheng Zou, Congcong Jia, Jinshui Yang, Changping Yin, Dingding Chen

“…Based on this correlation, a convolutional neural network (CNN) is employed to develop a classification model for evaluating the risk of interlaminar fracture. …”
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An Improved YOLOv8-Based Method for Detecting Pests and Diseases on Cucumber Leaves in Natural Backgrounds by Jiacong Xie, Xingliu Xie, Wu Xie, Qianxin Xie

“…First, the deformable convolution network DCNv2 (Deformable Convolution Network version 2) is introduced, replacing the original C2f module with an improved C2f_DCNv2 module in the backbone feature extraction network’s final C2f block. …”
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Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10 by Vasil Vasilev, Vasil Shterev, Maria Nenova

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The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index by Jicun Yang, Bing Guo, Rui Zhang

“…Modeling methods such as Convolutional Neural Networks (CNNs), Partial Least Squares Regression (PLSR), and Random Forest (RF) were employed to predict soil salinity. …”
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Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI by Lei Guo, Peisi Xie, Xionghui Shen, Thomas Ka Yam Lam, Lingli Deng, Chengyi Xie, Xiangnan Xu, Chris Kong Chu Wong, Jingjing Xu, Jiacheng Fang, Xiaoxiao Wang, Zhuang Xiong, Shangyi Luo, Jianing Wang, Jiyang Dong, Zongwei Cai

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Brain CT image classification based on mask RCNN and attention mechanism by Shoulin Yin, Hang Li, Lin Teng, Asif Ali Laghari, Ahmad Almadhor, Michal Gregus, Gabriel Avelino Sampedro

“…First, the ResNet-10 is utilized as the backbone model to extract local features of the input brain CT images. In the partial residual module, the standard convolution is substituted by deformable convolution. …”
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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion by Yuanbo Chu, Jiahao Wang, Longhui Ma, Chenxing Wu

“…Firstly, we propose a lightweight multiscale convolution (LMSConv) and a lightweight multiscale cross-stage partial (LMSCSP). …”
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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

“…Additionally, Omni-dimensional Dynamic Convolution was employed to optimize the model’s basic convolutional structure, bottleneck structure, and C2f (Faster Implementation of CSP (Cross Stage Partial) Bottleneck with two convolutions) module, improving feature fusion quality and reducing computational complexity. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…This framework model consists of three parts: a backbone network, a multi-scale feature representation network, and a detection head. Among them, partial convolutional (PConv) is utilized to construct a backbone network (P-Net), which ensures detection performance while further reducing computational complexity. …”
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Advanced AI-driven detection of cinnamon powder adulteration using near-infrared spectroscopy and deep learning image recognition technique by Hao-Hsiang Ku, Ya-Chuan Liao, Ching-Ho Chi

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Ghost Module-Enhanced MTCNN: A Lightweight Cascade Framework for High-Accuracy Face Detection in Edge-Deployable Scenarios by Chen Wang, Fen Liu

“…In this approach, the computationally intensive standard convolution layers in the Multi-task Cascaded Convolutional Neural Network (MTCNN) backbone are replaced by Ghost bottleneck modules from the GhostNet network, which offer lower computational requirements. …”
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Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations by Yaobin Hao, Fangying Song

“…The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. …”
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DEPDet: A Cross-Spatial Multiscale Lightweight Network for Ship Detection of SAR Images in Complex Scenes by Jing Zhang, Fan Deng, Yonghua Wang, Jie Gong, Ziyang Liu, Wenjun Liu, Yinmei Zeng, Zeqiang Chen

“…Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module PCHead, which can be employed to extract spatial features with greater efficiency. …”
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CNN for Computer Vision tasks by Р. Ковальчук, О. Польшакова

“…Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. …”
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A VVC intra coding method based on fast partition for coding unit by ZHONG Hui, LU Yu, YIN Haibing, HUANG Xiaofeng

“…Firstly, a light-weight convolutional neural network (CNN) network was used to predict partition for QT and partial MTT. …”
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