9 citations · 12 across the 15 of their papers we have counts for
15 papers
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Scalable Visual Pretraining for Language Intelligence
Yiming Zhang, Zhonghan Zhao, Wenwei Zhang +14
The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual…
DynaHOI: Benchmarking Hand-Object Interaction for Dynamic Target
BoCheng Hu, Zhonghan Zhao, Kaiyue Zhou +2
Most existing hand motion generation benchmarks for hand-object interaction (HOI) focus on static objects, leaving dynamic scenarios with moving targets and time-critical coordinat…
: A "Spot the Difference" Challenge for Large Multimodal Models
Kewei Wei, Bocheng Hu, Jie Cao +13
Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend…
Adaptive Graph Pruning for Multi-Agent Communication
Boyi Li, Zhonghan Zhao, Der-Horng Lee +1
Large Language Model (LLM) based multi-agent systems have shown remarkable performance in various tasks, especially when enhanced through collaborative communication. However, curr…
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy
Zhonghan Zhao, Wenwei Zhang, Haian Huang +4
Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either…
Ego3DT: Tracking Every 3D Object in Ego-centric Videos
Shengyu Hao, Wenhao Chai, Zhonghan Zhao +8
The growing interest in embodied intelligence has brought ego-centric perspectives to contemporary research. One significant challenge within this realm is the accurate localizatio…
STEVE Series: Step-by-Step Construction of Agent Systems in Minecraft
Zhonghan Zhao, Wenhao Chai, Xuan Wang +7
Building an embodied agent system with a large language model (LLM) as its core is a promising direction. Due to the significant costs and uncontrollable factors associated with de…
CityCraft: A Real Crafter for 3D City Generation
Jie Deng, Wenhao Chai, Junsheng Huang +9
City scene generation has gained significant attention in autonomous driving, smart city development, and traffic simulation. It helps enhance infrastructure planning and monitorin…
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
Zhonghan Zhao, Ke Ma, Wenhao Chai +6
With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output execut…
Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation
Zhonghan Zhao, Kewei Chen, Dongxu Guo +4
Due to the dynamic and unpredictable open-world setting, navigating complex environments in Minecraft poses significant challenges for multi-agent systems. Agents must interact wit…
See and Think: Embodied Agent in Virtual Environment
Zhonghan Zhao, Wenhao Chai, Xuan Wang +5
Large language models (LLMs) have achieved impressive pro-gress on several open-world tasks. Recently, using LLMs to build embodied agents has been a hotspot. This paper proposes S…
Devil in the Number: Towards Robust Multi-modality Data Filter
Yichen Xu, Zihan Xu, Wenhao Chai +3
In order to appropriately filter multi-modality data sets on a web-scale, it becomes crucial to employ suitable filtering methods to boost performance and reduce training costs. Fo…
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning
Meiqi Sun, Zhonghan Zhao, Wenhao Chai +5
Animal visual perception is an important technique for automatically monitoring animal health, understanding animal behaviors, and assisting animal-related research. However, it is…
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Zhonghan Zhao, Wenhao Chai, Shengyu Hao +6
Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive su…