Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

ArXi:2604.25188v1 Announce Type: new Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noise suppression are required simultaneously. Conventional convolutional neural networks, despite their remarkable success in hierarchical feature learning, often struggle with capturing multi-scale contextual information and are susceptible to overfitting when confronted with noisy or irrelevant image regions.