1 citations · 1 across the 5 of their papers we have counts for
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DroneWAM: Efficient World Action Model for Drone Visual Navigation
Liang Yao, Fan Liu, Hongbo Lu +5
World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences. For drones,…
RISE: Adaptive Imagination for World Action Models
Hongbo Lu, Liang Yao, Chenghao He +5
World Action Models (WAMs) improve planning by incorporating future world evolution into action generation, yet existing methods allocate a fixed imagination budget to every scene.…
RemoteZero: Geospatial Reasoning with Zero Labels
Liang Yao, Fan Liu, Shengxiang Xu +4
Geospatial reasoning requires models to identify image regions that satisfy complex and often implicit user intents. Recent reinforcement learning approaches improve reasoning with…
UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks
Liang Yao, Fan Liu, Shengxiang Xu +6
The development of multi-modal learning for Unmanned Aerial Vehicles (UAVs) typically relies on a large amount of pixel-aligned multi-modal image data. However, existing datasets f…
Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning
Fan Liu, Liang Yao, Chuanyi Zhang +4
Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream a…