most citedHiMemFormer: Hierarchical Memory-Aware Transformer for Multi-Agent Action Anticipation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

11 papers

cs.AI2025

Adaptively Coordinating with Novel Partners via Learned Latent Strategies

Benjamin Li, Shuyang Shi, Lucia Romero +7

Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…

cs.AI2025

Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration

Benjamin Li, Shuyang Shi, Lucia Romero +7

In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…

cs.CV2025

ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models

Zifu Wan, Ce Zhang, Silong Yong +6

Recent Large Vision-Language Models (LVLMs) have introduced a new paradigm for understanding and reasoning about image input through textual responses. Although they have achieved…

cs.CV2025

InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning

Zifu Wan, Yaqi Xie, Ce Zhang +5

Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, informati…

cs.CV2025

Spectral-Aware Global Fusion for RGB-Thermal Semantic Segmentation

Ce Zhang, Zifu Wan, Simon Stepputtis +2

Semantic segmentation relying solely on RGB data often struggles in challenging conditions such as low illumination and obscured views, limiting its reliability in critical applica…

cs.LG2025

Model-Agnostic Policy Explanations with Large Language Models

Zhang Xi-Jia, Yue Guo, Shufei Chen +4

Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, the…