39 citations · 57 across the 12 of their papers we have counts for
13 papers
ArmorOCR: Grounded Adversarial Visual Perception via Observation-Transferred Self-Distillation
Linhan Cao, Siyuan Li, Jun Lan +8
Large multimodal models (LMMs) have demonstrated strong OCR recognition capabilities, yet remain vulnerable to adversarial visual text that is readable to humans but challenging fo…
Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
Kailing Li, Tianwen Qian, Lijin Yang +4
Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions. Despite recent progress with vision-la…
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
Lai Wei, Liangbo He, Jun Lan +9
Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelme…
ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos
Qi'ao Xu, Tianwen Qian, Yuqian Fu +5
A core capability towards general embodied intelligence lies in localizing task-relevant objects from an egocentric perspective, formulated as Spatio-Temporal Video Grounding (STVG…
LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu +5
With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level…
CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation
Xiaoqi Li, Lingyun Xu, Mingxu Zhang +8
In robotic, task goals can be conveyed through various modalities, such as language, goal images, and goal videos. However, natural language can be ambiguous, while images or video…
DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
Xuemeng Yang, Licheng Wen, Yukai Ma +11
This paper presented DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. DriveArena features a flexible, mod…
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving
Jianbiao Mei, Yukai Ma, Xuemeng Yang +11
Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenar…
Human Simulacra: Benchmarking the Personification of Large Language Models
Qiuejie Xie, Qiming Feng, Tianqi Zhang +7
Large language models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted attention from the social science community,…
Towards Knowledge-driven Autonomous Driving
Xin Li, Yeqi Bai, Pinlong Cai +14
This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular…
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Licheng Wen, Daocheng Fu, Xin Li +7
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretab…
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
Peng Zhang, Xin Li, Liang He +1
3D multi-object tracking (3D MOT) stands as a pivotal domain within autonomous driving, experiencing a surge in scholarly interest and commercial promise over recent years. Despite…
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Tao Ma, Xuemeng Yang, Hongbin Zhou +9
Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D…