3 citations · 6 across the 17 of their papers we have counts for
17 papers
AgentVLN: Towards Agentic Vision-and-Language Navigation
Zihao Xin, Wentong Li, Yixuan Jiang +6
Vision-and-Language Navigation (VLN) requires an embodied agent to ground complex natural-language instructions into long-horizon navigation in unseen environments. While Vision-La…
DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving
Xiaolu Liu, Yicong Li, Song Wang +3
Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically predict future states through a…
Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
Song Wang, Lingdong Kong, Xiaolu Liu +4
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor…
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Tianshuai Hu, Xiaolu Liu, Song Wang +17
Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-ta…
VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration
Hanxun Yu, Wentong Li, Xuan Qu +3
Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing to…
RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
Sicheng Feng, Kaiwen Tuo, Song Wang +3
Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even adva…