9 citations · 11 across the 8 of their papers we have counts for
8 papers
OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models
Tianran Liu, Shengwen Zhao, Mozhgan Pourkeshavarz +2
Data-driven autonomous driving simulation has long been constrained by its heavy reliance on pre-recorded driving logs or spatial priors, such as HD maps. This fundamental dependen…
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mozhgan Pourkeshavarz, Tianran Liu, Nicholas Rhinehart
Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent behavior from higher-level abstractions…
Improving Robotic Manipulation Robustness via NICE Scene Surgery
Sajjad Pakdamansavoji, Mozhgan Pourkeshavarz, Adam Sigal +3
Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety.…
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
Junrui Zhang, Mozhgan Pourkeshavarz, Amir Rasouli
As a safety critical task, autonomous driving requires accurate predictions of road users' future trajectories for safe motion planning, particularly under challenging conditions.…
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving
Changhe Chen, Mozhgan Pourkeshavarz, Amir Rasouli
Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scena…
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
Mozhgan Pourkeshavarz, Mohammad Sabokrou, Amir Rasouli
In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramo…
Stacked Cross-modal Feature Consolidation Attention Networks for Image Captioning
Mozhgan Pourkeshavarz, Shahabedin Nabavi, Mohsen Ebrahimi Moghaddam +1
Recently, the attention-enriched encoder-decoder framework has aroused great interest in image captioning due to its overwhelming progress. Many visual attention models directly le…
ZS-IL: Looking Back on Learned Experiences For Zero-Shot Incremental Learning
Mozhgan PourKeshavarz, Mohammad Sabokrou
Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance d…