Ruixun Liu — accepted AI-conference papers

2026

ZoomEarth: Active Perception for Ultra-High-Resolution Geospatial Vision-Language Tasks

Ruixun Liu, Bowen Fu, Jiayi Song, Kaiyu Li, Wanchen Li, Lanxuan Xue +4

CVPR 2026

Ultra-high-resolution (UHR) remote sensing (RS) images offer rich fine-grained information but also present challenges in effective processing. Existing dynamic resolution and token pruning methods are constrained by a passive perception paradigm, suffering from increased redundancy when obtaining f

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2025

State Revisit and Re-explore: Bridging Sim-to-Real Gaps in Offline-and-Online Reinforcement Learning with An Imperfect Simulator

Xingyu Chen, Jiayi Xie, Zhijian Xu, Ruixun Liu, Shuai Yang, Zeyang Liu +2

IJCAI 2025

In reinforcement learning (RL) based robot skill acquisition, a high-fidelity simulator is usually indispensable but unattainable since the real environment dynamics are difficult to model, which leads to severe sim-to-real gaps. Existing methods solve this problem by combining offline and online RL

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