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
FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention
Xiwei Liu, Min Xu, Qirong Ho
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks
Pandiyaraju V, Santhosh Malarvannan, Shravan Venkatraman, Abeshek A, Priyadarshini B, Kannan A
BiDense: Binarization for Dense Prediction
Rui Yin, Haotong Qin, Yulun Zhang, Wenbo Li, Yong Guo, Jianjun Zhu, Cheng Wang, Biao Jia
CNN-Based Classification of Persian Miniature Paintings from Five Renowned Schools
Mojtaba Shahi, Roozbeh Rajabi, Farnaz Masoumzadeh
STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing
Andrea Alfarano, Alberto Alfarano, Linda Friso, Andrea Bacciu, Irene Amerini, Fabrizio Silvestri
On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks
Sagar Ghosh, Kushal Bose, Swagatam Das
Flow reconstruction in time-varying geometries using graph neural networks
Bogdan A. Danciu, Vito A. Pagone, Benjamin Böhm, Marius Schmidt, Christos E. Frouzakis
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios
Muttahirul Islam, Nazmul Haque, Md. Hadiuzzaman
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection
Aditya V. Jonnalagadda, Hashim A. Hashim, Andrew Harris
Semantic segmentation on multi-resolution optical and microwave data using deep learning
Jai G Singla, Bakul Vaghela
LAUREL: Learned Augmented Residual Layer
Gaurav Menghani, Ravi Kumar, Sanjiv Kumar
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci
Spatially Constrained Transformer with Efficient Global Relation Modelling for Spatio-Temporal Prediction
Ashutosh Sao, Simon Gottschalk
ScaleKD: Strong Vision Transformers Could Be Excellent Teachers
Jiawei Fan, Chao Li, Xiaolong Liu, Anbang Yao
KLCBL: An Improved Police Incident Classification Model
Liu Zhuoxian, Shi Tuo, Hu Xiaofeng