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
Image Classification for Snow Detection to Improve Pedestrian Safety
Ricardo de Deijn, Rajeev Bukralia
Analysis of Modern Computer Vision Models for Blood Cell Classification
Alexander Kim, Ryan Kim
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
Bas Peters, Eldad Haber, Keegan Lensink
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
Aidan Furlong, Farah Alsafadi, Scott Palmtag, Andrew Godfrey, Xu Wu
CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network
Jia-Hau Bai, Chi-Ting Liu, Yu Wang, Fu-Chieh Chang, Pei-Yuan Wu
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks
I. R. Slootweg, M. Thach, K. R. Curro-Tafili, F. D. Verbraak, F. H. Bouwman, Y. A. L. Pijnenburg, J. F. Boer, J. H. P. de Kwisthout, L. Bagheriye, P. J. González
EFCNet: Every Feature Counts for Small Medical Object Segmentation
Lingjie Kong, Qiaoling Wei, Chengming Xu, Han Chen, Yanwei Fu
Camera Model Identification Using Audio and Visual Content from Videos
Ioannis Tsingalis, Christos Korgialas, Constantine Kotropoulos
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge
John Violos, Symeon Papadopoulos, Ioannis Kompatsiaris
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet
Manish Dhakal, Arman Chhetri, Aman Kumar Gupta, Prabin Lamichhane, Suraj Pandey, Subarna Shakya
SUM: Saliency Unification through Mamba for Visual Attention Modeling
Alireza Hosseini, Amirhossein Kazerouni, Saeed Akhavan, Michael Brudno, Babak Taati
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)
Julien Taran
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models
Mohammad Belal, Taimur Hassan, Abdelfatah Ahmed, Ahmad Aljarah, Nael Alsheikh, Irfan Hussain
Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces
Zhaohui Chen, Elyas Asadi Shamsabadi, Sheng Jiang, Luming Shen, Daniel Dias-da-Costa