4 papers
A4-Agent: An Agentic Framework for Zero-Shot Affordance Reasoning
Zixin Zhang, Kanghao Chen, Hanqing Wang +5
Affordance prediction, which identifies interaction regions on objects based on language instructions, is critical for embodied AI. Prevailing end-to-end models couple high-level r…
HOID-R1: Reinforcement Learning for Open-World Human-Object Interaction Detection Reasoning with Multimodal Large Language Model
Zhenhao Zhang, Hanqing Wang, Xiangyu Zeng +10
Understanding and recognizing human-object interaction (HOI) is a pivotal application in AR/VR and robotics. Recent open-vocabulary HOI detection approaches depend exclusively on l…
SDEval: Safety Dynamic Evaluation for Multimodal Large Language Models
Hanqing Wang, Yuan Tian, Mingyu Liu +2
In the rapidly evolving landscape of Multimodal Large Language Models (MLLMs), the safety concerns of their outputs have earned significant attention. Although numerous datasets ha…
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
Chunlin Yu, Hanqing Wang, Ye Shi +4
3D affordance segmentation aims to link human instructions to touchable regions of 3D objects for embodied manipulations. Existing efforts typically adhere to single-object, single…