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
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
María Teresa García-Ordás, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides, Héctor Alaiz-Moretón
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
Wenjia Xu, Jiuniu Wang, Zhiwei Wei, Mugen Peng, Yirong Wu
FuseFormer: A Transformer for Visual and Thermal Image Fusion
Aytekin Erdogan, Erdem Akagündüz
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
Christoph Lange, Isabel Thiele, Lara Santolin, Sebastian L. Riedel, Maxim Borisyak, Peter Neubauer, M. Nicolas Cruz Bournazou
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Jacob Fein-Ashley, Sachini Wickramasinghe, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna
Towards Visual Syntactical Understanding
Sayeed Shafayet Chowdhury, Soumyadeep Chandra, Kaushik Roy
Efficient Gesture Recognition on Spiking Convolutional Networks Through Sensor Fusion of Event-Based and Depth Data
Lea Steffen, Thomas Trapp, Arne Roennau, Rüdiger Dillmann
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation
Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël Phan
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Hongyu Zhao, Zezhi Tang, Zhenhong Li, Yi Dong, Yuancheng Si, Mingyang Lu, George Panoutsos
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
Serdar Erisen
A New Method for Vehicle Logo Recognition Based on Swin Transformer
Yang Li, Doudou Zhang, Jianli Xiao
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval
Ayush Dubey, Shiv Ram Dubey, Satish Kumar Singh, Wei-Ta Chu
ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation
Hongkun Sun, Jing Xu, Yuping Duan
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones
Beatrice Alessandra Motetti, Luca Crupi, Mustafa Omer Mohammed Elamin Elshaigi, Matteo Risso, Daniele Jahier Pagliari, Daniele Palossi, Alessio Burrello
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Chunhua Zhou, Fengyu Sun, Di Niu
Do deep neural networks utilize the weight space efficiently?
Onur Can Koyun, Behçet Uğur Töreyin