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
Ophthalmic Biomarker Detection with Parallel Prediction of Transformer and Convolutional Architecture
Md. Touhidul Islam, Md. Abtahi Majeed Chowdhury, Mahmudul Hasan, Asif Quadir, Lutfa Aktar
Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes
Seraj Ghasemi, Hamed Hosseini, MohammadHossein Koosheshi, Mehdi Tale Masouleh, Ahmad Kalhor
EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation
Ao Chang, Jiajun Zeng, Ruobing Huang, Dong Ni
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution
Zhenyu Hu, Wanjie Sun
The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach
David Bertoin, Eduardo Hugo Sanchez, Mehdi Zouitine, Emmanuel Rachelson
CNN Mixture-of-Depths
Rinor Cakaj, Jens Mehnert, Bin Yang
Linking in Style: Understanding learned features in deep learning models
Maren H. Wehrheim, Pamela Osuna-Vargas, Matthias Kaschube
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, Juoli 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