14 citations · 15 across the 7 of their papers we have counts for
7 papers
LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences
Hongyan Zhi, Peihao Chen, Junyan Li +6
Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing attention, which is crucial for developing embodied AI within 3D scenes, such as visual navigation and embodie…
FlexAttention for Efficient High-Resolution Vision-Language Models
Junyan Li, Delin Chen, Tianle Cai +5
Current high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increa…
Disentangled Acoustic Fields For Multimodal Physical Scene Understanding
Jie Yin, Andrew Luo, Yilun Du +4
We study the problem of multimodal physical scene understanding, where an embodied agent needs to find fallen objects by inferring object properties, direction, and distance of an…
CoNav: A Benchmark for Human-Centered Collaborative Navigation
Changhao Li, Xinyu Sun, Peihao Chen +6
Human-robot collaboration, in which the robot intelligently assists the human with the upcoming task, is an appealing objective. To achieve this goal, the agent needs to be equippe…
3D-VLA: A 3D Vision-Language-Action Generative World Model
Haoyu Zhen, Xiaowen Qiu, Peihao Chen +5
Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by le…
MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World
Yining Hong, Zishuo Zheng, Peihao Chen +3
Human beings possess the capability to multiply a melange of multisensory cues while actively exploring and interacting with the 3D world. Current multi-modal large language models…
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
Kunyang Lin, Yufeng Wang, Peihao Chen +4
Learning optimal behavior policy for each agent in multi-agent systems is an essential yet difficult problem. Despite fruitful progress in multi-agent reinforcement learning, the c…