Search Results for author:
Found 7 papers, 4 papers with code
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
While transformers have achieved state-of-the-art results in natural language processing and image recognition, they face challenges in medical image segmentation due to image locality and translational invariance issues.
Traffic Sign Recognition Using Local Vision Transformer
The experimental evaluations demonstrate that the hybrid network with the locality module outperforms pure transformer-based models and some of the best convolutional networks in accuracy.
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition
The teacher model extracts local image features, whereas the student model focuses on global features using an attention mechanism.
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture
Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties.
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis.
Robust Transformer with Locality Inductive Bias and Feature Normalization
In this paper, we explore the robustness of vision transformers against adversarial perturbations and try to enhance their robustness/accuracy trade-off in white box attack settings.
Pyramid Transformer for Traffic Sign Detection
The results demonstrate the superiority of the proposed model in the traffic sign detection tasks.
