2 citations · 6 across the 13 of their papers we have counts for
6 papers · 1 filter
Efficient-VLN: A Simple yet Strong Baseline for Efficient Vision-Language Navigation
Duo Zheng, Shijia Huang, Yanyang Li +1
While Multimodal Large Language Models (MLLMs) have demonstrated significant promise in Vision-Language Navigation (VLN), existing agents remain heavily constrained by systemic bot…
Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors
Duo Zheng, Shijia Huang, Yanyang Li +1
Previous research has investigated the application of Multimodal Large Language Models (MLLMs) in understanding 3D scenes by interpreting them as videos. These approaches generally…
Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
Duo Zheng, Shijia Huang, Liwei Wang
The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require s…
Towards Learning a Generalist Model for Embodied Navigation
Duo Zheng, Shijia Huang, Lin Zhao +2
Building a generalist agent that can interact with the world is the intriguing target of AI systems, thus spurring the research for embodied navigation, where an agent is required…
Towards Unifying Reference Expression Generation and Comprehension
Duo Zheng, Tao Kong, Ya Jing +2
Reference Expression Generation (REG) and Comprehension (REC) are two highly correlated tasks. Modeling REG and REC simultaneously for utilizing the relation between them is a prom…
Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue
Zipeng Xu, Fandong Meng, Xiaojie Wang +3
To encourage AI agents to conduct meaningful Visual Dialogue (VD), the use of Reinforcement Learning has been proven potential. In Reinforcement Learning, it is crucial to represen…