activity
20232025
most citedDriveGPT: Scaling Autoregressive Behavior Models for Driving

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Generative Data Mining with Longtail-Guided Diffusion

David S. Hayden, Mao Ye, Timur Garipov +6

It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, d…

cs.LG2024★ 1 cited

DriveGPT: Scaling Autoregressive Behavior Models for Driving

Xin Huang, Eric M. Wolff, Paul Vernaza +13

We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent…

cs.CV2024

PROFIT: A Specialized Optimizer for Deep Fine Tuning

Anirudh S Chakravarthy, Shuai Kyle Zheng, Xin Huang +4

The fine-tuning of pre-trained models has become ubiquitous in generative AI, computer vision, and robotics. Although much attention has been paid to improving the efficiency of fi…

cs.CV2024

VLMine: Long-Tail Data Mining with Vision Language Models

Mao Ye, Gregory P. Meyer, Zaiwei Zhang +4

Ensuring robust performance on long-tail examples is an important problem for many real-world applications of machine learning, such as autonomous driving. This work focuses on the…

cs.CV2024

Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving

Yichen Xie, Hongge Chen, Gregory P. Meyer +6

Due to the lack of depth cues in images, multi-frame inputs are important for the success of vision-based perception, prediction, and planning in autonomous driving. Observations f…

cs.CV2023

ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

Mu Cai, Haotian Liu, Dennis Park +4

While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches t…

cs.CV2023

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors

Hongge Chen, Zhao Chen, Gregory P. Meyer +4

We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors. In safety-critical applications like…