Convolutional Neural Network
Convolutional Neural Networks (CNNs) are a class of deep learning models designed for processing grid-like data, excelling in image analysis and related tasks. Current research focuses on improving CNN efficiency and robustness, exploring architectures like EfficientNet and Swin Transformers, as well as novel approaches such as Mamba models to address limitations in computational cost and long-range dependency capture. This active field of research has significant implications across diverse applications, including medical image analysis (e.g., cancer detection, Alzheimer's diagnosis), damage assessment, and art forgery detection, demonstrating the power of CNNs for automating complex visual tasks.
Papers
Pavement Fatigue Crack Detection and Severity Classification Based on Convolutional Neural Network
Zhen Wang, Dylan G. Ildefonzo, Linbing Wang
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani, Adetokunbo Arogbonlo, Siamak Pedrammehr, Adnan Anwar, Asim Bhatti, Saeid Nahavandi, Chee Peng Lim
Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns
Christos Kyrkou
Subgraph Clustering and Atom Learning for Improved Image Classification
Aryan Singh, Pepijn Van de Ven, Ciarán Eising, Patrick Denny
Early Detection of Coffee Leaf Rust Through Convolutional Neural Networks Trained on Low-Resolution Images
Angelly Cabrera, Kleanthis Avramidis, Shrikanth Narayanan
Improving Representation of High-frequency Components for Medical Foundation Models
Yuetan Chu, Yilan Zhang, Zhongyi Han, Changchun Yang, Longxi Zhou, Gongning Luo, Xin Gao
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models
Samay Nathani
EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition
Youssef Doulfoukar, Laurent Mertens, Joost Vennekens
Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network
Alexis Abad, Alexandre Poux, Alexis Boulet, Marc Brunel
DuoFormer: Leveraging Hierarchical Visual Representations by Local and Global Attention
Xiaoya Tang, Bodong Zhang, Beatrice S. Knudsen, Tolga Tasdizen
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking
Yunpeng Gong, Chuangliang Zhang, Yongjie Hou, Lifei Chen, Min Jiang
Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting
Sihao Li, Kyeong Soo Kim, Zhe Tang, Graduate, Jeremy S. Smith
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network
Hao Yan, Zixiang Wang, Zhengjia Xu, Zhuoyue Wang, Zhizhong Wu, Ranran Lyu
Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection
Pandiyaraju V, Shravan Venkatraman, Abeshek A, Pavan Kumar S, Aravintakshan S A
Detecting Omissions in Geographic Maps through Computer Vision
Phuc D. A. Nguyen, Anh Do, Minh Hoai
An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots
J. J. Cabrera, O. J. Céspedes, S. Cebollada, O. Reinoso, L. Payá
An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments
J. J. Cabrera, V. Román, A. Gil, O. Reinoso, L. Payá