IEEE

MonoICT: A Monocular 3-D Object Detection Model Integrating CNN and Transformer

Abstract: In the field of autonomous driving, 3-D object detection is a crucial technology. Visual sensors are essential in this area and are widely used for 3-D object detection tasks. Recent ...
IEEE

Development of Scalable Deep Learning Frameworks Utilizing Advanced Hybrid Models like Transformer-CNN for Distributed Computing Environments

Abstract: Over the past few years especially in the context of communication and information processing the importance of Natural language processing which demands efficient deep learning models has ...
IEEE

MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone’s Potential with Masked Autoregressive Pretraining

Abstract: Hybrid Mamba-Transformer networks have recently garnered broad attention. These networks can leverage the scalability of Transformers while capitalizing on Mamba’s strengths in long-context ...
IEEE

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Abstract: We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually ...
IEEE

Object Detection using Vision Transformer and Deep Learning for Computer Vision Applications

Abstract: Vision Transformer (ViT) is an image recognition model that uses transformer architecture, which has a numerous advantage over Convolution Neural Networks (CNN). It offers improved accuracy, ...
IEEE

Transformer and CNN-Based Deep Learning Models for Skin Lesion Segmentation

Abstract: Skin cancer is a common cancer, and early detection is vital for effective treatment. Automated skin cancer segmentation, especially through deep learning models, has gained significant ...
IEEE

LightFormer: Vision Transformer for Lightweight Based on Cascade Depthwise Convolution and Mixed Attention

Abstract: In recent years, the complementary advantages of convolutional neural networks (CNNs) and Transformers have been utilized to achieve significant results in image classification tasks.
IEEE

BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification

Abstract: Vision transformers have demonstrated remarkable performance in hyperspectral image classification tasks. However, their complex computational mechanisms and excessive parameterization ...
IEEE

Hybrid Vision Transformers and Federated Learning for Precision and Privacy in Skin Lesion Detection

Abstract: Skin cancer is a serious health concern globally, and melanoma is the cause of most deaths as it is usually diagnosed too late or misdiagnosed. Early diagnosis of skin lesion identifications ...
IEEE

Hybrid CNN-Transformer Based Sparse Channel Prediction for High-Mobility OTFS Systems

Abstract: High-mobility scenarios in next-generation wireless networks, such as those involving vehicular communications, require ultra-reliable and low-latency communications (URLLC). However, ...
IEEE

Advancing Brain Tumor Detection via ViRCNN: A Fusion of Vision Transformers and Faster R-CNN

Abstract: Accurate brain tumor detection is vital for effective diagnosis and treatment. This paper presents ViRCNN, a hybrid model that integrates Faster R-CNN and Vision Transformers (ViT) to ...
IEEE

Hybrid CNN-Transformer Model for Accurate Classification of Human Attention Levels Using Workplace EEG Data

Abstract: Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) ...