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20172023
most citedUnsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

317 citations · 1.2k across the 45 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

cs.CV2023★ 50 cited

SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images

Haonan Guo, Xin Su, Chen Wu +2

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically…

cs.CV2023★ 1 cited

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

Haonan Guo, Bo Du, Chen Wu +2

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently…

cs.LG2023

Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers

Yineng Chen, Zuchao Li, Lefei Zhang +2

Optimizer is an essential component for the success of deep learning, which guides the neural network to update the parameters according to the loss on the training set. SGD and Ad…

cs.CV2023

Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision

Haonan Guo, Bo Du, Chen Wu +2

The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness. Many existing building segmentation methods were deve…

cs.CL2023★ 1 cited

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction

Tianshuo Peng, Zuchao Li, Lefei Zhang +2

Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, U…

cs.CV2023★ 50 cited

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

Di Wang, Jing Zhang, Bo Du +4

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with anno…