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20162024
most citedMARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

242 citations · 481 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024★ 1 cited

NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation

Youzhi Liu, Fanglong Yao, Yuanchang Yue +3

Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environmen…

cs.CV2020★ 18 cited

Where is the Model Looking At?--Concentrate and Explain the Network Attention

Wenjia Xu, Jiuniu Wang, Yang Wang +3

Image classification models have achieved satisfactory performance on many datasets, sometimes even better than human. However, The model attention is unclear since the lack of int…

cs.CV2019★ 4 cited

Deep Discriminative Representation Learning with Attention Map for Scene Classification

Jun Li, Daoyu Lin, Yang Wang +2

Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were b…

cs.CV2019★ 75 cited

A Remote Sensing Image Dataset for Cloud Removal

Daoyu Lin, Guangluan Xu, Xiaoke Wang +3

Cloud-based overlays are often present in optical remote sensing images, thus limiting the application of acquired data. Removing clouds is an indispensable pre-processing step in…

cs.CV2019★ 5 cited

Wider Channel Attention Network for Remote Sensing Image Super-resolution

Jun Gu, Guangluan Xu, Yue Zhang +3

Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods tr…

cs.CV2016★ 242 cited

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

Daoyu Lin, Kun Fu, Yang Wang +2

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the l…