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
Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography
J. Toivanen, V. Kolehmainen, A. Paldanius, A. Hänninen, A. Hauptmann, S.J. Hamilton
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning
Smruti Jagtap, Kanika Jadhav, Rushikesh Temkar, Minal Deshmukh
Post-Training Non-Uniform Quantization for Convolutional Neural Networks
Ahmed Luqman, Khuzemah Qazi, Imdadullah Khan
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications
Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo, Khatalyn E. Mata, Jonathan C. Morano
Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness
Longwei Wang, Xueqian Li, Zheng Zhang
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang, Lin Zhu, Bo Jiang, Yonghong Tian
Detecting Facial Image Manipulations with Multi-Layer CNN Models
Alejandro Marco Montejano, Angela Sanchez Perez, Javier Barrachina, David Ortiz-Perez, Manuel Benavent-Lledo, Jose Garcia-Rodriguez
Inverting Visual Representations with Detection Transformers
Jan Rathjens, Shirin Reyhanian, David Kappel, Laurenz Wiskott
Mask of truth: model sensitivity to unexpected regions of medical images
Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez, Jack Junchi Xu, Veronika Cheplygina
Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systems
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe, M. Victoria Martínez, Inés del Campo
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
Loukas Ilias, George Doukas, Vangelis Lamprou, Christos Ntanos, Dimitris Askounis
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Ajinkya Deshpande, Deep Gupta, Ankit Bhurane, Nisha Meshram, Sneha Singh, Petia Radeva
Enhancing Crop Segmentation in Satellite Image Time Series with Transformer Networks
Ignazio Gallo, Mattia Gatti, Nicola Landro, Christian Loschiavo, Mirco Boschetti, Riccardo La Grassa
Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs
Aniket K. Singh, Debasis Chaudhuri, Manish P. Singh, Samiran Chattopadhyay
Reducing Inference Energy Consumption Using Dual Complementary CNNs
Michail Kinnas, John Violos, Ioannis Kompatsiaris, Symeon Papadopoulos