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
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval
Amirreza Mahbod, Nematollah Saeidi, Sepideh Hatamikia, Ramona Woitek
AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging
Andrea Dosi, Massimo Brescia, Stefano Cavuoti, Mariarca D'Aniello, Michele Delli Veneri, Carlo Donadio, Adriano Ettari, Giuseppe Longo, Alvi Rownok, Luca Sannino, Maria Zampella
D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segmentation
Jin Yang, Xiaobing Yu, Peijie Qiu
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data
Sigmund Slang, Jing Sun, Thomas Elboth, Steven McDonald, Leiv-J. Gelius
HTR-VT: Handwritten Text Recognition with Vision Transformer
Yuting Li, Dexiong Chen, Tinglong Tang, Xi Shen
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
Le Zhang, Onat Gungor, Flavio Ponzina, Tajana Rosing
A convolutional neural network approach to deblending seismic data
Jing Sun, Sigmund Slang, Thomas Elboth, Thomas Larsen Greiner, Steven McDonald, Leiv-J Gelius
A framework for measuring the training efficiency of a neural architecture
Eduardo Cueto-Mendoza, John D. Kelleher
Violence detection in videos using deep recurrent and convolutional neural networks
Abdarahmane Traoré, Moulay A. Akhloufi
TabMixer: Noninvasive Estimation of the Mean Pulmonary Artery Pressure via Imaging and Tabular Data Mixing
Michal K. Grzeszczyk, Przemysław Korzeniowski, Samer Alabed, Andrew J. Swift, Tomasz Trzciński, Arkadiusz Sitek
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
Pranav Rama, Madison Threadgill, Andreas Gerstlauer
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network
Md Taimur Ahad, Sajib Bin Mamun, Sumaya Mustofa, Bo Song, Yan Li
A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)
Sabit Ahamed Preanto (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md. Taimur Ahad (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Yousuf Rayhan Emon (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Sumaya Mustofa (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md Alamin (4IR Research Cell Daffodil International University, Dhaka, Bangladesh)
Lightweight single-image super-resolution network based on dual paths
Li Ke, Liu Yukai
Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
David Millard, Arielle Carr, Stéphane Gaudreault
Seam Carving as Feature Pooling in CNN
Mohammad Imrul Jubair