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20232026
most citedA New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection

3 citations · 7 across the 21 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CV2024

Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method

Pan Yin, Kaiyu Li, Xiangyong Cao +5

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting roa…

cs.CV2024

SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images

Kaiyu Li, Ruixun Liu, Xiangyong Cao +4

Remote sensing image plays an irreplaceable role in fields such as agriculture, water resources, military, and disaster relief. Pixel-level interpretation is a critical aspect of r…

cs.CV2024

MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption

Ruixun Liu, Kaiyu Li, Jiayi Song +2

Remote sensing image change caption (RSICC) aims to provide natural language descriptions for bi-temporal remote sensing images. Since Change Caption (CC) task requires both spatia…

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★ 2 cited

SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector

Kaiyu Li, Xiangyong Cao, Yupeng Deng +4

Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especia…

cs.CV2024

Class Similarity Transition: Decoupling Class Similarities and Imbalance from Generalized Few-shot Segmentation

Shihong Wang, Ruixun Liu, Kaiyu Li +2

In Generalized Few-shot Segmentation (GFSS), a model is trained with a large corpus of base class samples and then adapted on limited samples of novel classes. This paper focuses o…