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
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
Wei Peng, Kang Liu, Jiaxi Shi, Jianchen Hu
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
Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation
Xiaowei Yu, Zhe Huang, Zao Zhang
Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)
Faisal Mehmood, Xin Guo, Enqing Chen, Muhammad Azeem Akbar, Arif Ali Khan, Sami Ullah
RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Boyao Wang, Volodymyr Kindratenko
SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM
Shuang Chen, Haozheng Zhang, Amir Atapour-Abarghouei, Hubert P. H. Shum
Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction
Masoud Kargar, Nasim Jelodari, Alireza Assadzadeh
Parallel Multi-path Feed Forward Neural Networks (PMFFNN) for Long Columnar Datasets: A Novel Approach to Complexity Reduction
Ayoub Jadouli, Chaker El Amrani
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise
Moseli Mots'oehli, kyungim Baek
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla
DeepArUco++: Improved detection of square fiducial markers in challenging lighting conditions
Rafael Berral-Soler, Rafael Muñoz-Salinas, Rafael Medina-Carnicer, Manuel J. Marín-Jiménez