84 citations · 199 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
Junyan Li, Delin Chen, Yining Hong +4
A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundatio…
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation
Xinyu Sun, Peihao Chen, Jugang Fan +3
Learning to navigate to an image-specified goal is an important but challenging task for autonomous systems. The agent is required to reason the goal location from where a picture…
Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models
Peihao Chen, Xinyu Sun, Hongyan Zhi +5
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by langu…
Learning Vision-and-Language Navigation from YouTube Videos
Kunyang Lin, Peihao Chen, Diwei Huang +3
Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from train…
Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition
Weidong Chen, Xiaofen Xing, Peihao Chen +1
This paper presents a paradigm that adapts general large-scale pretrained models (PTMs) to speech emotion recognition task. Although PTMs shed new light on artificial general intel…
Detecting the open-world objects with the help of the Brain
Shuailei Ma, Yuefeng Wang, Ying Wei +5
Open World Object Detection (OWOD) is a novel computer vision task with a considerable challenge, bridging the gap between classic object detection (OD) benchmarks and real-world o…