39 citations · 55 across the 8 of their papers we have counts for
13 papers
RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents
Fuyuan Liu, Nayu Liu, Wenhao Yu +5
Remote sensing agents rely on perception, measurement, and raster analysis tools to solve Earth observation tasks. We refer to their judgments and quantitative results about ground…
RingMoClaw: An Experience-Inspired Multi-Agent Framework for Self-Evolving Research in Remote Sensing
Kaiyue Kang, Qixuan He, Peijin Wang +9
Remote sensing visual models have continuously advanced various interpretation tasks. However, the research process behind model improvement still heavily relies on manual expertis…
Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents
Zhijian Li, Chao Ren, Peijin Wang +1
Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners often rely on predefined maps and…
GeoForge: Non-Parametric Self-Evolving Agents for Earth-Observation Reasoning
Xin Xiao, Jiang Zhong, Junnan Zhu +4
Earth observation (EO) agents construct scientifically valid tool workflows and ground their conclusions in current geospatial evidence. This is challenging because EO workflows ar…
CFVBench: A Comprehensive Video Benchmark for Fine-grained Multimodal Retrieval-Augmented Generation
Kaiwen Wei, Xiao Liu, Jie Zhang +11
Multimodal Retrieval-Augmented Generation (MRAG) enables Multimodal Large Language Models (MLLMs) to generate responses with external multimodal evidence, and numerous video-based…
RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning
Huiyang Hu, Peijin Wang, Yingchao Feng +9
Remote sensing (RS) images from multiple modalities and platforms exhibit diverse details due to differences in sensor characteristics and imaging perspectives. Existing vision-lan…
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning
Mengyu Wang, Hanbo Bi, Yingchao Feng +7
Vision foundation models in remote sensing have been extensively studied due to their superior generalization on various downstream tasks. Synthetic Aperture Radar (SAR) offers all…
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
Hanbo Bi, Yingchao Feng, Boyuan Tong +11
The rapid advancement of foundation models has revolutionized visual representation learning in a self-supervised manner. However, their application in remote sensing (RS) remains…
RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model
Huiyang Hu, Peijin Wang, Hanbo Bi +10
Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, th…
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation
Hanbo Bi, Yingchao Feng, Wenhui Diao +5
For more efficient generalization to unseen domains (classes), most Few-shot Segmentation (FSS) would directly exploit pre-trained encoders and only fine-tune the decoder, especial…
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection
Xiaonan Lu, Wenhui Diao, Yongqiang Mao +4
Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learn…
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
Xian Sun, Peijin Wang, Zhiyuan Yan +11
With the rapid development of deep learning, many deep learning-based approaches have made great achievements in object detection task. It is generally known that deep learning is…
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
Ziqi Zhang, Yaya Shi, Chunfeng Yuan +4
Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neg…