16 citations · 39 across the 8 of their papers we have counts for
8 papers
Particle-Filtering-based Latent Diffusion for Inverse Problems
Amir Nazemi, Mohammad Hadi Sepanj, Nicholas Pellegrino +2
Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling.However, almost all approaches make no explicit attempt to e…
Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data
Dylan Spicker, Amir Nazemi, Joy Hutchinson +4
Dietary intake data are routinely drawn upon to explore diet-health relationships. However, these data are often subject to measurement error, distorting the true relationships. Be…
Memory-Efficient Continual Learning Object Segmentation for Long Video
Amir Nazemi, Mohammad Javad Shafiee, Zahra Gharaee +1
Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from pre…
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
Amir Nazemi, Zeyad Moustafa, Paul Fieguth
Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the…
A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study
Milad Ramezankhani, Amir Nazemi, Apurva Narayan +4
Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accur…
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi +2
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Potential adversarial samples for white-box attacks
Amir Nazemi, Paul Fieguth
Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep net…
Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition
Amir Nazemi, Mohammad Javad Shafiee, Zohreh Azimifar +1
Vehicle Make and Model Recognition (MMR) systems provide a fully automatic framework to recognize and classify different vehicle models. Several approaches have been proposed to ad…