1 citations · 1 across the 12 of their papers we have counts for
12 papers
Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Zhenxin Li, Nadine Chang, Xinglong Sun +8
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implaus…
HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving
Wenhao Yao, Xinglong Sun, Zhenxin Li +4
End-to-end planning has emerged as a dominant paradigm for autonomous driving, where recent models often adopt a scoring-selection framework to choose trajectories from a large set…
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Zhenxin Li, Nadine Chang, Wenhao Yao +9
Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for long-tail and safety-critical scenarios…
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
Zuyao You, Zuxuan Wu
We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LM…
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Zhenxin Li, Wenhao Yao, Zi Wang +7
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory s…
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
Wenhao Yao, Zhenxin Li, Shiyi Lan +4
Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly asses…
Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
Kailin Li, Zhenxin Li, Shiyi Lan +6
Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a ligh…
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
Zhenxin Li, Shihao Wang, Shiyi Lan +3
End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are traine…
Pix2Cap-COCO: Advancing Visual Comprehension via Pixel-Level Captioning
Zuyao You, Junke Wang, Lingyu Kong +2
We present Pix2Cap-COCO, the first panoptic pixel-level caption dataset designed to advance fine-grained visual understanding. To achieve this, we carefully design an automated ann…
FOCUS: Towards Universal Foreground Segmentation
Zuyao You, Lingyu Kong, Lingchen Meng +1
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for e…
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Zhenxin Li, Shiyi Lan, Jose M. Alvarez +1
Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Ey…
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
Wujian Peng, Sicheng Xie, Zuyao You +2
Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attri…