22 citations · 57 across the 7 of their papers we have counts for
8 papers
Language Conditioned Traffic Generation
Shuhan Tan, Boris Ivanovic, Xinshuo Weng +2
Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment…
EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding
Shuhan Tan, Tushar Nagarajan, Kristen Grauman
Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications. To address this challen…
TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
Lan Feng, Quanyi Li, Zhenghao Peng +2
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called Traff…
SceneGen: Learning to Generate Realistic Traffic Scenes
Shuhan Tan, Kelvin Wong, Shenlong Wang +3
We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics…
Improving the Fairness of Deep Generative Models without Retraining
Shuhan Tan, Yujun Shen, Bolei Zhou
Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data. Despite the high-quality generated faces, some minority…
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong +6
We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of preference for most self-driving vehicles. We argue that, by leveraging real data, we…
Class-imbalanced Domain Adaptation: An Empirical Odyssey
Shuhan Tan, Xingchao Peng, Kate Saenko
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However, the current literature assumes that the label distribution is domain-invarian…
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Shuhan Tan, Jiening Jiao, Wei-Shi Zheng
In conventional domain adaptation, a critical assumption is that there exists a fully labeled domain (source) that contains the same label space as another unlabeled or scarcely la…