activity
20192026
most citedObject Detection in 20 Years: A Survey

559 citations · 1k across the 66 of their papers we have counts for

collaborators
Showing cs.CVShow all

60 papers · 1 filter

cs.CV2026

Remote Sensing Sparse-View 3D Gaussian Splatting via Depth Image-Based Rendering

Jiaming Kang, Zhengxia Zou, Zhenwei Shi

Remote sensing novel view synthesis under sparse observations remains challenging due to insufficient geometric constraints and limited cross-view supervision. Existing Neural Radi…

cs.CV2026

LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching

Siwei Yu, Han Guo, Zhenwei Shi +1

Dense image matching establishes pixel-wise correspondences and underpins broad applications in computer vision and photogrammetry. However, extending dense matching to global-scal…

cs.CV2026

FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detection

Haotian Zhang, Hao Chen, Han Guo +2

Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at each spatial location over…

cs.CV2026

LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection

Qinzhe Yang, Dongyu Wang, Haohan Niu +3

Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existing datasets and detectors rem…

cs.CV2026

State Space Models Meet Remote Sensing: A Survey

Qinzhe Yang, Chenyang Liu, Jia Xu +2

State Space Models (SSMs), designed for long-range modeling, offer linear computational complexity and strong capabilities in capturing long-range dependencies. In the field of rem…

cs.CV2026

Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models

Qinzhe Yang, Keyan Chen, Jia Xu +2

The computational complexity of Transformers scales quadratically with the number of tokens, which significantly constrains the efficiency of vision models, particularly recent ViT…

cs.CV2026

SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation

Chuyu Zhong, Keyan Chen, Qinzhe Yang +3

Pixel count and geographical coverage are two key characteristics of remote sensing images. Existing remote sensing image segmentation methods typically focus on images with either…

cs.CV2026

MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling

Zhiping Yu, Chenyang Liu, Jinqi Cao +4

Multi-modal remote sensing images are vital for Earth observation, yet complete paired observations are often scarce in practice. Existing generative methods commonly address this…

cs.CV2026

MetaEarth3D: Unlocking World-scale 3D Generation with Spatially Scalable Generative Modeling

Jinqi Cao, Zhiping Yu, Baihong Lin +3

Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate realistic visual content, their…

cs.CV2026

CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery

Jiajun Yang, Keyan Chen, Zhengxia Zou +1

Cloud detection in remote sensing imagery is a fundamental, critical, and highly challenging problem. Existing deep learning-based cloud detection methods generally formulate it as…

cs.CV2025

TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection

Han Guo, Chenyang Liu, Haotian Zhang +3

Remote sensing change detection (RSCD) aims to identify surface changes across bi-temporal satellite images. Most previous methods rely solely on mask supervision, which effectivel…

cs.CV2025

FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection

Haotian Zhang, Han Guo, Keyan Chen +3

Despite the remarkable progress achieved in remote sensing semantic change detection (SCD), two major challenges remain. At the data level, existing SCD datasets suffer from limite…

cs.CV2025★ 5 cited

RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models

Keyan Chen, Chenyang Liu, Bowen Chen +3

Referring Remote Sensing Image Segmentation provides a flexible and fine-grained framework for remote sensing scene analysis via vision-language collaborative interpretation. Curre…

cs.CV2025★ 6 cited

SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model

Bowen Chen, Keyan Chen, Mohan Yang +2

High-resolution (HR) remote sensing imagery plays a vital role in a wide range of applications, including urban planning and environmental monitoring. However, due to limitations i…

cs.CV2025

BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting

Yongchang Wu, Zipeng Qi, Zhenwei Shi +1

The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated remarkable potential in novel view synthesis tasks. The divide-and-conquer paradigm has enabled large-scal…

cs.CV2025

DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models

Keyan Chen, Chenyang Liu, Bowen Chen +4

The advancement of RS technology has enabled high-resolution Earth observation; however, interpreting these images using modern VFMs remains a significant challenge. Unlike object-…

cs.CV2025

TriDF: Triplane-Accelerated Density Fields for Few-Shot Remote Sensing Novel View Synthesis

Jiaming Kang, Keyan Chen, Zhengxia Zou +1

Remote sensing novel view synthesis (NVS) offers significant potential for 3D interpretation of remote sensing scenes, with important applications in urban planning and environment…

cs.CV2025

Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields from In-Situ Observations

Zili Liu, Hao Chen, Lei Bai +3

Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric cha…

cs.CV2025

RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models

Keyan Chen, Jiafan Zhang, Chenyang Liu +2

Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extract…

cs.CV2025★ 1 cited

Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

Chenyang Liu, Keyan Chen, Rui Zhao +2

Generative foundation models have advanced large-scale text-driven natural image generation, becoming a prominent research trend across various vertical domains. However, in the re…

cs.CV2024

Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation

Zipeng Qi, Hao Chen, Haotian Zhang +2

In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic…

cs.CV2024★ 30 cited

Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey

Chenyang Liu, Jiafan Zhang, Keyan Chen +3

The interpretation of multi-temporal remote sensing imagery is critical for monitoring Earth's dynamic processes-yet previous change detection methods, which produce binary or sema…

cs.CV2024

Open-CD: A Comprehensive Toolbox for Change Detection

Kaiyu Li, Jiawei Jiang, Andrea Codegoni +13

We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of…

cs.CV2024

A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection

Chenyao Zhou, Haotian Zhang, Han Guo +2

Semantic change detection is an important task in geoscience and earth observation. By producing a semantic change map for each temporal phase, both the land use land cover categor…