Vision-Language-Action (VLA) models present a promising paradigm for general-purpose robot manipulation, but closed-loop deployment is often limited by inference efficiency: at each timestep, high-resolution, often multi-view images are encoded into hundreds of visual tokens that dominate transformer computation and la...
Jiu-Yi Xu, Cheng-Yuan Qian, Zheng-Zhong Tu et al.· 0 citations
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorrectly inferred by foundation models. This paper presents a lightweight, training-free, and uncertainty-aware framework for cooperative multi...
Fan Pu, Keshu Wu, Ji-Wan Jiang et al.· 0 citations
CinematicVQA is introduced, the first-of-its-kind benchmark for cinematic video understanding that goes beyond technique recognition to evaluate film-grammar reasoning, utilizing the introduced Cinematic Scene Graph (CSG), a structured representation that links filming techniques to their perceptual effects and narrati...
Shuo Xing, Pooja Verlani, Balu Adsumilli et al.· 0 citations
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
Yu-Ping Wang, Shuo Xing, C. Cui et al.· ACM Computing Surveys· 60 citations· ⚡2
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