Abstract Vision Transformer (ViT) has gained increasing attention in the computer vision
community in recent years. However the core component of ViT Self-Attention lacks explicit …

Abstract This paper revives Densely Connected Convolutional Networks (DenseNets) and
reveals the underrated effectiveness over predominant ResNet-style architectures. We …

Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently
introduced to address the quadratic complexity of the attention mechanism and …

Recently some large kernel convnets strike back with appealing performance and efficiency.
However given the square complexity of convolution scaling up kernels can bring about an …

Aiming at the problems of low detection efficiency and poor detection accuracy caused by
texture feature interference and dramatic changes in the scale of defect on steel surfaces, an …

Abstract Deep learning and Convolutional Neural Networks (CNNs) have driven major
transformations in diverse research areas. However, their limitations in handling low …

We introduce a novel architecture design that enhances expressiveness by incorporating
multiple head classifiers (ie, classification heads) instead of relying on channel expansion or …

Object detection in remote sensing images (RSIs) often suffers from several increasing
challenges including the large variation in object scales and the diverse-ranging context …

Recent years have witnessed a growing interest in neural network-based medical image
classification methods, which have demonstrated remarkable performance in this field …

To address the current difficulties in fire detection algorithms, including inadequate feature
extraction, excessive computational complexity, limited deployment on devices with limited …