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20242026
most citedA Survey of Data Agents: Emerging Paradigm or Overstated Hype?

1 citations · 4 across the 28 of their papers we have counts for

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cs.CV2026

AI-generated Images Challenge Visual Trust in High-risk Scenarios

Yi-Zhi Wang, Yichen Xiao, Linan Yue +5

Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing…

cs.CV2026

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…

cs.CV2026

RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation

Rui Min, Liang Yao, Shiyu Miao +5

A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input variations. However, current Rem…

cs.CV2026

RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs

Liang Yao, Shengxiang Xu, Fan Liu +7

Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language rather than precise, machine-f…

cs.CV2025

RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow

Liang Yao, Fan Liu, Hongbo Lu +5

Remote sensing imagery presents vast, inherently unstructured spatial data, necessitating sophisticated reasoning to interpret complex user intents and contextual relationships bey…

cs.CV2025

RemoteSAM: Towards Segment Anything for Earth Observation

Liang Yao, Fan Liu, Delong Chen +6

We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets wh…

cs.CV2024★ 1 cited

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…