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
ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
Shihua Sun, Kenechukwu Nwodo, Shridatt Sugrim, Angelos Stavrou, Haining Wang
Keypoint Detection Technique for Image-Based Visual Servoing of Manipulators
Niloufar Amiri, Guanghui Wang, Farrokh Janabi-Sharifi
Analyzing the Effect of $k$-Space Features in MRI Classification Models
Pascal Passigan, Vayd Ramkumar
Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks
Guibiao Liao, Wei Gao
Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities
Chengkun Sun, Jinqian Pan, Zhuoli Jin, Russell Stevens Terry, Jiang Bian, Jie Xu
Semi-Supervised Semantic Segmentation with Professional and General Training
Yuting Hong, Hui Xiao, Huazheng Hao, Xiaojie Qiu, Baochen Yao, Chengbin Peng
Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection
Mujadded Al Rabbani Alif
MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation
Chenyuan Bian, Nan Xia, Xia Yang, Feifei Wang, Fengjiao Wang, Bin Wei, Qian Dong
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection
Weijie He, Runyuan Bao, Yiru Cang, Jianjun Wei, Yang Zhang, Jiacheng Hu
On Vision Transformers for Classification Tasks in Side-Scan Sonar Imagery
BW Sheffield, Jeffrey Ellen, Ben Whitmore
Memory Networks: Towards Fully Biologically Plausible Learning
Jacobo Ruiz, Manas Gupta
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
Rong Zhou, Zhengqing Yuan, Zhiling Yan, Weixiang Sun, Kai Zhang, Yiwei Li, Yanfang Ye, Xiang Li, Lifang He, Lichao Sun
Complex-valued convolutional neural network classification of hand gesture from radar images
Shokooh Khandan
Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image
Dewinda Julianensi Rumala, Reza Fuad Rachmadi, Anggraini Dwi Sensusiati, I Ketut Eddy Purnama
WaterQualityNeT: Prediction of Seasonal Water Quality of Nepal Using Hybrid Deep Learning Models
Biplov Paneru, Bishwash Paneru
SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance
Shun Zou, Mingya Zhang, Bingjian Fan, Zhengyi Zhou, Xiuguo Zou