most citedPassage Segmentation of Documents for Extractive Question Answering

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2025

It Takes Two: Learning Interactive Whole-Body Control Between Humanoid Robots

Zuhong Liu, Junhao Ge, Minhao Xiong +4

The true promise of humanoid robotics lies beyond single-agent autonomy: two or more humanoids must engage in physically grounded, socially meaningful whole-body interactions that…

cs.RO2025

Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

Junhao Ge, Zuhong Liu, Longteng Fan +5

End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale real-world data…

cs.CL20251 cited

Passage Segmentation of Documents for Extractive Question Answering

Zuhong Liu, Charles-Elie Simon, Fabien Caspani

Retrieval-Augmented Generation (RAG) has proven effective in open-domain question answering. However, the chunking process, which is essential to this pipeline, often receives insu…

cs.CV2024

Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals

Shaoheng Fang, Zuhong Liu, Mingyu Wang +3

Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly…

cs.CV2024

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

Shaoheng Fang, Rui Ye, Wenhao Wang +5

Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single R…