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
On the VC dimension of deep group convolutional neural networks
Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber
Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws
Hao Wang, Jiajun Zhong, Yikun Li, Junrong Zhang, Rong Du
Disambiguating Monocular Reconstruction of 3D Clothed Human with Spatial-Temporal Transformer
Yong Deng, Baoxing Li, Xu Zhao
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
Jintao Ren, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Silin Chen, Ming Li, Jiawei Xu, Ming Liu
Advancing Gasoline Consumption Forecasting: A Novel Hybrid Model Integrating Transformers, LSTM, and CNN
Mahmoud Ranjbar, Mohammad Rahimzadeh
Taming Mambas for Voxel Level 3D Medical Image Segmentation
Luca Lumetti, Vittorio Pipoli, Kevin Marchesini, Elisa Ficarra, Costantino Grana, Federico Bolelli
AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images
Omar Hesham Khater, Abdullahi Sani Shuaib, Sami Ul Haq, Abdul Jabbar Siddiqui
Self Supervised Deep Learning for Robot Grasping
Danyal Saqib, Wajahat Hussain
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation
Teerath Kumar, Alessandra Mileo, Malika Bendechache
Accelerating Object Detection with YOLOv4 for Real-Time Applications
K. Senthil Kumar, K.M.B. Abdullah Safwan
Latent Image and Video Resolution Prediction using Convolutional Neural Networks
Rittwika Kansabanik, Adrian Barbu
Machine learning approach to brain tumor detection and classification
Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant
Guopeng Li, Qiang Wang, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia
Stress Assessment with Convolutional Neural Network Using PPG Signals
Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
Xiannan Huang, Chao Yang, Quan Yuan