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
Learning Color Equivariant Representations
Felix O'Mahony, Yulong Yang, Christine Allen-Blanchette
Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs
Jan Steckel, Arne Aerts, Erik Verreycken, Dennis Laurijssen, Walter Daems
Computer Vision Approaches for Automated Bee Counting Application
Simon Bilik, Ilona Janakova, Adam Ligocki, Dominik Ficek, Karel Horak
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning
Yuxiang Hu, Jinxin Hu, Ting Xu, Bo Zhang, Jiajie Yuan, Haozhang Deng
Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning
Dan Sun, Yaxin Liang, Yining Yang, Yuhan Ma, Qishi Zhan, Erdi Gao
Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network
Houze Liu, Iris Li, Yaxin Liang, Dan Sun, Yining Yang, Haowei Yang
UruBots Autonomous Car Team Two: Team Description Paper for FIRA 2024
William Moraes, Juan Deniz, Pablo Moraes, Christopher Peters, Vincent Sandin, Gabriel da Silva, Franco Nunez, Maximo Retamar, Victoria Saravia, Hiago Sodre, Sebastian Barcelona, Anthony Scirgalea, Bruna Guterres, Andre Kelbouscas, Ricardo Grando
Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification
Md. Mahmudul Hasan, SM Shaqib, Ms. Sharmin Akter, Rabiul Alam, Afraz Ul Haque, Shahrun akter khushbu
AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI
Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua, Yassine Himeur
Multi-Objective Neural Architecture Search for In-Memory Computing
Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
Leonardo Scabini, Andre Sacilotti, Kallil M. Zielinski, Lucas C. Ribas, Bernard De Baets, Odemir M. Bruno
Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM
Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque, Mahjub Alam Khan Hesham, Tanvir Ahmed
MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification
Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma, Amir Houmansadr
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh
Utilizing Grounded SAM for self-supervised frugal camouflaged human detection
Matthias Pijarowski, Alexander Wolpert, Martin Heckmann, Michael Teutsch
Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D MRI Scans
Muthukumar K A, Amit Gurung, Priya Ranjan
Navigating Efficiency in MobileViT through Gaussian Process on Global Architecture Factors
Ke Meng, Kai Chen
Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models
Malo Maisonneuve, Corinne Fredouille, Muriel Lalain, Alain Ghio, Virginie Woisard