16 citations · 21 across the 28 of their papers we have counts for
31 papers
RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts
Hongbin Lin, Chaoda Zheng, Yiming Yang +11
End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their own actions, leading to caus…
InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
Xiaoyu Ye, Leheng Li, Xinyu Ji +8
Generating realistic, controllable, and temporally coherent urban environments is a critical yet unresolved challenge in the autonomous driving community. In this paper, we introdu…
VesSAM: Efficient Multi-Prompting for Segmenting Complex Vessel
Suzhong Fu, Rui Sun, Xuan Ding +8
Accurate vessel segmentation is critical for clinical applications such as disease diagnosis and surgical planning, yet remains challenging due to thin, branching structures and lo…
FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
Hongbin Lin, Yiming Yang, Yifan Zhang +10
In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive relian…
DriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving
Hongbin Lin, Yiming Yang, Chaoda Zheng +7
In autonomous driving, vision-centric 3D object detection recognizes and localizes 3D objects from RGB images. However, due to high annotation costs and diverse outdoor scenes, tra…
SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving
Haiming Zhang, Yiyao Zhu, Wending Zhou +5
Sparse Perception Models (SPMs) adopt a query-driven paradigm that forgoes explicit dense BEV or volumetric construction, enabling highly efficient computation and accelerated infe…
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
Mingcong Lei, Honghao Cai, Yuyuan Yang +16
Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with parti…
FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models
Yiming Yang, Hongbin Lin, Yueru Luo +7
Lane segment topology reasoning provides comprehensive bird's-eye view (BEV) road scene understanding, which can serve as a key perception module in planning-oriented end-to-end au…
TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving
Yiming Yang, Yueru Luo, Bingkun He +8
Lane segment topology reasoning constructs a comprehensive road network by capturing the topological relationships between lane segments and their semantic types. This enables end-…
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning with Lightweight Annotations
Junjie Wang, Zhihao Yuan, Shuyi Jiang +5
Large language models are increasingly being applied to 3D scene understanding. However, existing 3D-aware LLMs largely operate as black boxes: they produce bounding boxes or textu…
Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes
Yueru Luo, Changqing Zhou, Yiming Yang +4
Road topology reasoning is fundamental for autonomous driving, requiring both accurate perception of road elements and understanding of their complex connectivity, including lane c…
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation
Hongbin Lin, Zilu Guo, Yifan Zhang +5
In autonomous driving, vision-centric 3D detection aims to identify 3D objects from images. However, high data collection costs and diverse real-world scenarios limit the scale of…
Adaptive Pruning for Large Language Models with Structural Importance Awareness
Haotian Zheng, Jinke Ren, Yushan Sun +6
The recent advancements in large language models (LLMs) have significantly improved language understanding and generation capabilities. However, it is difficult to deploy LLMs on r…
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training
Haiming Zhang, Ying Xue, Xu Yan +6
The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper…
Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
Chaoda Zheng, Feng Wang, Naiyan Wang +2
While 3D object bounding box (bbox) representation has been widely used in autonomous driving perception, it lacks the ability to capture the precise details of an object's intrins…
D-World: An Efficient World Model through Decoupled Dynamic Flow
Haiming Zhang, Xu Yan, Ying Xue +4
This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems. We i…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
Haiming Zhang, Wending Zhou, Yiyao Zhu +7
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that…
MixPolyp: Integrating Mask, Box and Scribble Supervision for Enhanced Polyp Segmentation
Yiwen Hu, Jun Wei, Yuncheng Jiang +4
Limited by the expensive labeling, polyp segmentation models are plagued by data shortages. To tackle this, we propose the mixed supervised polyp segmentation paradigm (MixPolyp).…
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection
Yuncheng Jiang, Zixun Zhang, Jun Wei +5
AI-assisted lesion detection models play a crucial role in the early screening of cancer. However, previous image-based models ignore the inter-frame contextual information present…
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
Yuncheng Jiang, Yiwen Hu, Zixun Zhang +7
Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the la…
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
Yueru Luo, Shuguang Cui, Zhen Li
Accurate 3D lane estimation is crucial for ensuring safety in autonomous driving. However, prevailing monocular techniques suffer from depth loss and lighting variations, hampering…
Fully Test-Time Adaptation for Monocular 3D Object Detection
Hongbin Lin, Yifan Zhang, Shuaicheng Niu +2
Monocular 3D object detection (Mono 3Det) aims to identify 3D objects from a single RGB image. However, existing methods often assume training and test data follow the same distrib…
SA-GS: Semantic-Aware Gaussian Splatting for Large Scene Reconstruction with Geometry Constrain
Butian Xiong, Xiaoyu Ye, Tze Ho Elden Tse +3
With the emergence of Gaussian Splats, recent efforts have focused on large-scale scene geometric reconstruction. However, most of these efforts either concentrate on memory reduct…