11 citations · 19 across the 17 of their papers we have counts for
7 papers · 1 filter
Diffusion Models with Deterministic Normalizing Flow Priors
Mohsen Zand, Ali Etemad, Michael Greenspan
For faster sampling and higher sample quality, we propose DiNof (ffusion with rmalizing low priors), a technique that makes use of normalizing…
Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction
Mohsen Zand, Ali Etemad, Michael Greenspan
A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at highe…
Learning Better Keypoints for Multi-Object 6DoF Pose Estimation
Yangzheng Wu, Michael Greenspan
We address the problem of keypoint selection, and find that the performance of 6DoF pose estimation methods can be improved when pre-defined keypoint locations are learned, rather…
Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer
Haleh Damirchi, Michael Greenspan, Ali Etemad
We propose a novel solution for predicting future trajectories of pedestrians. Our method uses a multimodal encoder-decoder transformer architecture, which takes as input both pede…
Continual Learning for Out-of-Distribution Pedestrian Detection
Mahdiyar Molahasani, Ali Etemad, Michael Greenspan
A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved…
On The Relationship Between Continual Learning and Long-Tailed Recognition
Mahdiyar Molahasani, Michael Greenspan, Ali Etemad
Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, lead…
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection
Andrew Farley, Mohsen Zand, Michael Greenspan
We propose a method that augments a simulated dataset using diffusion models to improve the performance of pedestrian detection in real-world data. The high cost of collecting and…