80 citations · 110 across the 13 of their papers we have counts for
14 papers
RS-Claw-Evolution: Environment-Feedback-Driven Evolution for Lightweight Remote Sensing Agents in Long-Horizon Tasks
Kai Ouyang, Dongyang Hou, Liangtian Liu +9
Large language model-driven remote sensing (RS) agents offer a promising approach to automating geospatial analysis. However, lightweight RS agents based on compact language models…
Think with Extra-Image: A Farmland Segmentation Agent Driven by Spatio-Temporal Information Gain
Haiyang Wu, Weiliang Mu, Zhuofei Du +4
Existing farmland remote sensing image (FRSI) segmentation follows a "Think with Intra-Image" paradigm, assuming that the current image contains sufficient visual evidence for reli…
iGSP:Implicit Gradient Subspace Projection for Efficient Continual Learning of Vision-Language Models
Xuezhi Cui, Dongbo Zhou, Wang Guo +8
Vision-Language Models require efficient adaptation to continually emerging downstream tasks. While Parameter-Efficient Fine-Tuning mitigates catastrophic forgetting, assigning iso…
RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents
Liangtian Liu, Zeyuan Wang, Ziyu Li +8
The rise of multi-modal large language models (MLLMs) is shifting remote sensing (RS) intelligence from "see" to "action", as OpenClaw-style frameworks enable agents to autonomousl…
The Wittgensteinian Representation Hypothesis: Is Language the Attractor of Multimodal Convergence?
Zhaoyang Zhang, Run Shao, Dongyue Wu +4
Understanding why independently trained neural networks from different modalities converge toward shared representations, and where this convergence leads, remains an open question…
Towards Accurate UAV Image Perception: Guiding Vision-Language Models with Stronger Task Prompts
Mingning Guo, Mengwei Wu, Shaoxian Li +2
Existing image perception methods based on VLMs generally follow a paradigm wherein models extract and analyze image content based on user-provided textual task prompts. However, s…
Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges
Haifeng Li, Wang Guo, Haiyang Wu +6
The mainstream paradigm of remote sensing image interpretation has long been dominated by vision-centered models, which rely on visual features for semantic understanding. However,…
BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs
Mingning Guo, Mengwei Wu, Jiarun He +3
With the rapid advancement of low-altitude remote sensing and Vision-Language Models (VLMs), Embodied Agents based on Unmanned Aerial Vehicles (UAVs) have shown significant potenti…
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images
Yuze Wang, Rong Xiao, Haifeng Li +2
In remote sensing scene classification, leveraging the transfer methods with well-trained optical models is an efficient way to overcome label scarcity. However, cloud contaminatio…
IFShip: Interpretable Fine-grained Ship Classification with Domain Knowledge-Enhanced Vision-Language Models
Mingning Guo, Mengwei Wu, Yuxiang Shen +2
End-to-end interpretation currently dominates the remote sensing fine-grained ship classification (RS-FGSC) task. However, the inference process remains uninterpretable, leading to…
SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want
Ling Zhao, Zhenyang Huang, Dongsheng Kuang +3
The existing change detection(CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences an…
Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
Run Shao, Zhaoyang Zhang, Chao Tao +3
The tokenizer, as one of the fundamental components of large models, has long been overlooked or even misunderstood in visual tasks. One key factor of the great comprehension power…
HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection
Chao Tao, Dongsheng Kuang, Zhenyang Huang +2
In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there ar…
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
Haifeng Li, Xin Dou, Chao Tao +5
In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via…