5 papers
: Toward Versatile Embodied Agents
Shengli Zhou, Xiangchen Wang, Jinrui Zhang +5
Embodied agents have demonstrated promising capabilities in interacting with physical environments. Yet, versatile embodied agents face three core bottlenecks: dynamic environmenta…
CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models
Shengli Zhou, Xiangchen Wang, Guanhua Chen +1
Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene g…
Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models
Shengli Zhou, Minghang Zheng, Feng Zheng +1
Spatial reasoning focuses on locating target objects based on spatial relations in 3D scenes, which plays a crucial role in developing intelligent embodied agents. Due to the limit…
Learn 3D VQA Better with Active Selection and Reannotation
Shengli Zhou, Yang Liu, Feng Zheng
3D Visual Question Answering (3D VQA) is crucial for enabling models to perceive the physical world and perform spatial reasoning. In 3D VQA, the free-form nature of answers often…
HCNQA: Enhancing 3D VQA with Hierarchical Concentration Narrowing Supervision
Shengli Zhou, Jianuo Zhu, Qilin Huang +3
3D Visual Question-Answering (3D VQA) is pivotal for models to perceive the physical world and perform spatial reasoning. Answer-centric supervision is a commonly used training met…