Unsupervised model adaptation for continual semantic segmentation
We develop an algorithm for adapting a semantic segmentation model that is trained using a
labeled source domain to generalize well in an unlabeled target domain. A similar problem …
labeled source domain to generalize well in an unlabeled target domain. A similar problem …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Increasing model generalizability for unsupervised visual domain adaptation
M Rostami - Conference on Lifelong Learning Agents, 2022 - proceedings.mlr.press
A dominant approach for addressing unsupervised domain adaptation is to map data points
for the source and the target domains into an embedding space which is modeled as the …
for the source and the target domains into an embedding space which is modeled as the …
Improved region proposal network for enhanced few-shot object detection
Z Shangguan, M Rostami - Neural Networks, 2024 - Elsevier
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 …
of deep neural networks requires access to a substantial quantity of annotated images …
Transfer learning via representation learning
The remarkable performance boost of artificial intelligence (AI) algorithms is a result of re-
emergence of deep neural networks that have been applied in a diverse set of applications …
emergence of deep neural networks that have been applied in a diverse set of applications …
Unsupervised model adaptation for source-free segmentation of medical images
The recent prevalence of deep neural networks has led semantic segmentation networks to
achieve human-level performance in the medical field, provided they are given sufficient …
achieve human-level performance in the medical field, provided they are given sufficient …
Multi-source data integration for segmentation of unannotated mri images
N Nananukul, H Soltanian-Zadeh… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep
neural networks greatly assists in evaluating and planning treatments for various clinical …
neural networks greatly assists in evaluating and planning treatments for various clinical …
Noise-residual mixup for unsupervised adversarial domain adaptation
C He, T Tan, X Fan, L Zheng, Z Ye - Applied Intelligence, 2023 - Springer
Unsupervised domain adaptation (UDA) methods based on deep adversarial learning are
successful for many practical fields. The deep adversarial UDA methods can promote …
successful for many practical fields. The deep adversarial UDA methods can promote …
[图书][B] Transfer learning through embedding spaces
M Rostami - 2021 - books.google.com
Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI
algorithms have reached human-level performance and AI agents are replacing humans in …
algorithms have reached human-level performance and AI agents are replacing humans in …
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation
M Rostami, D Jian - arXiv preprint arXiv:2401.01042, 2024 - arxiv.org
Event-based cameras provide accurate and high temporal resolution measurements for
performing computer vision tasks in challenging scenarios, such as high-dynamic range …
performing computer vision tasks in challenging scenarios, such as high-dynamic range …