5 citations · 21 across the 20 of their papers we have counts for
8 papers · 1 filter
An Intermediate Fusion ViT Enables Efficient Text-Image Alignment in Diffusion Models
Zizhao Hu, Shaochong Jia, Mohammad Rostami
Diffusion models have been widely used for conditional data cross-modal generation tasks such as text-to-image and text-to-video. However, state-of-the-art models still fail to ali…
Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks
Yuliang Cai, Mohammad Rostami
Transformer neural networks are increasingly replacing prior architectures in a wide range of applications in different data modalities. The increasing size and computational deman…
Unsupervised Domain Adaptation Using Compact Internal Representations
Mohammad Rostami
A major technique for tackling unsupervised domain adaptation involves mapping data points from both the source and target domains into a shared embedding space. The mapping encode…
Unsupervised Federated Domain Adaptation for Segmentation of MRI Images
Navapat Nananukul, Hamid Soltanian-zadeh, Mohammad Rostami
Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical app…
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
Serban Stan, Mohammad Rostami
Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training…
Improved Region Proposal Network for Enhanced Few-Shot Object Detection
Zeyu Shangguan, Mohammad Rostami
Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images…