88 citations · 104 across the 13 of their papers we have counts for
18 papers
RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents
Fuyuan Liu, Nayu Liu, Wenhao Yu +8
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…
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…
DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV
Tong Ling, Wenhui Diao, Yingchao Feng +3
Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practical deployments, UAVs operate…
Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues
Hanbo Bi, Zhiqiang Yuan, Chongyang Li +7
With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. Users often need to retrieve…
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…
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
Hanbo Bi, Yulong Xu, Ya Li +8
The Segment Anything Model (SAM), with its prompt-driven paradigm, exhibits strong generalization in generic segmentation tasks. However, applying SAM to remote sensing (RS) images…
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…
AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing
Hanbo Bi, Yingchao Feng, Yongqiang Mao +4
Few-shot Segmentation (FSS) aims to segment the interested objects in the query image with just a handful of labeled samples (i.e., support images). Previous schemes would leverage…
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…
RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning
Wenhui Diao, Haichen Yu, Kaiyue Kang +8
Aerial Remote Sensing (ARS) vision tasks present significant challenges due to the unique viewing angle characteristics. Existing research has primarily focused on algorithms for s…
Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
Hanbo Bi, Yingchao Feng, Zhiyuan Yan +4
Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics fro…
Double Similarity Distillation for Semantic Image Segmentation
Yingchao Feng, Xian Sun, Wenhui Diao +2
The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case o…
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…
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
Yingchao Feng, Wenhui Diao, Zhonghan Chang +3
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neura…