2022
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![]() | Li, Jun; Wu, Zhaocong; Hu, Zhongwen; Jian, Canliang; Luo, Shaojie; Mou, Lichao; Zhu, Xiao Xiang; Molinier, Matthieu A Lightweight Deep Learning-Based Cloud Detection Method for Sentinel-2A Imagery Fusing Multiscale Spectral and Spatial Features Journal Article IEEE Transactions on Geoscience and Remote Sensing, 60 , pp. 1–19, 2022. Links | BibTeX | Tags: @article{Li_2022,
title = {A Lightweight Deep Learning-Based Cloud Detection Method for Sentinel-2A Imagery Fusing Multiscale Spectral and Spatial Features},
author = {Jun Li and Zhaocong Wu and Zhongwen Hu and Canliang Jian and Shaojie Luo and Lichao Mou and Xiao Xiang Zhu and Matthieu Molinier},
url = {https://doi.org/10.1109%2Ftgrs.2021.3069641},
doi = {10.1109/tgrs.2021.3069641},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {60},
pages = {1--19},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Heidler, Konrad; Mou, Lichao; Baumhoer, Celia; Dietz, Andreas; Zhu, Xiao Xiang HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline Journal Article IEEE Transactions on Geoscience and Remote Sensing, 60 , pp. 1-14, 2022. Links | BibTeX | Tags: @article{9383809,
title = {HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline},
author = {Konrad Heidler and Lichao Mou and Celia Baumhoer and Andreas Dietz and Xiao Xiang Zhu},
doi = {10.1109/TGRS.2021.3064606},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {60},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Hua, Yuansheng; Marcos, Diego; Mou, Lichao; Zhu, Xiao Xiang; Tuia, Devis Semantic Segmentation of Remote Sensing Images With Sparse Annotations Journal Article IEEE Geoscience and Remote Sensing Letters, 19 , pp. 1-5, 2022. Links | BibTeX | Tags: @article{9335495,
title = {Semantic Segmentation of Remote Sensing Images With Sparse Annotations},
author = {Yuansheng Hua and Diego Marcos and Lichao Mou and Xiao Xiang Zhu and Devis Tuia},
doi = {10.1109/LGRS.2021.3051053},
year = {2022},
date = {2022-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {19},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Sun, Yao; Hua, Yuansheng; Mou, Lichao; Zhu, Xiao Xiang CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 60 , pp. 1-15, 2022. Links | BibTeX | Tags: @article{9321533,
title = {CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images},
author = {Yao Sun and Yuansheng Hua and Lichao Mou and Xiao Xiang Zhu},
doi = {10.1109/TGRS.2020.3043089},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {60},
pages = {1-15},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Geiß, Christian; Zhu, Yue; Qiu, Chunping; Mou, Lichao; Zhu, Xiao Xiang; Taubenböck, Hannes Deep Relearning in the Geospatial Domain for Semantic Remote Sensing Image Segmentation Journal Article IEEE Geoscience and Remote Sensing Letters, 19 , pp. 1-5, 2022. Links | BibTeX | Tags: @article{9247397,
title = {Deep Relearning in the Geospatial Domain for Semantic Remote Sensing Image Segmentation},
author = {Christian Geiß and Yue Zhu and Chunping Qiu and Lichao Mou and Xiao Xiang Zhu and Hannes Taubenböck},
doi = {10.1109/LGRS.2020.3031339},
year = {2022},
date = {2022-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {19},
pages = {1-5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Zaidi, Syed Ali S; Fraz, Muhammad Moazam; Shahzad, Muhammad; Khan, Sharifullah A multiapproach generalized framework for automated solution suggestion of support tickets Journal Article International Journal of Intelligent Systems, 37 (6), pp. 3654-3681, 2022. Abstract | Links | BibTeX | Tags: @article{https://doi.org/10.1002/int.22701,
title = {A multiapproach generalized framework for automated solution suggestion of support tickets},
author = {Syed S Ali Zaidi and Muhammad Moazam Fraz and Muhammad Shahzad and Sharifullah Khan},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.22701},
doi = {https://doi.org/10.1002/int.22701},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Intelligent Systems},
volume = {37},
number = {6},
pages = {3654-3681},
abstract = {Abstract Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk. |
![]() | L., Xia Zhu Jin Mou G X P Anomaly Detection in Aerial Videos with Transformers Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2022, (in press). BibTeX | Tags: @article{,
title = {Anomaly Detection in Aerial Videos with Transformers},
author = {Xia Zhu G X Jin P. Mou L.},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
note = {in press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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![]() | Guo, Jianhua; Xu, Qingsong; Zeng, Yue; Liu, Zhiheng; Zhu, Xiaoxiang Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem Journal Article Remote Sensing, 14 (11), 2022, ISSN: 2072-4292. Abstract | Links | BibTeX | Tags: @article{rs14112641,
title = {Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem},
author = {Jianhua Guo and Qingsong Xu and Yue Zeng and Zhiheng Liu and Xiaoxiang Zhu},
url = {https://www.mdpi.com/2072-4292/14/11/2641},
doi = {10.3390/rs14112641},
issn = {2072-4292},
year = {2022},
date = {2022-01-01},
journal = {Remote Sensing},
volume = {14},
number = {11},
abstract = {In terms of semi-supervised cloud detection work, efforts are being made to learn a promising cloud detection model via a limited number of pixel-wise labeled images and a large number of unlabeled ones. However, remote sensing images obtained from the same satellite sensor often show a data distribution drift problem due to the different cloud shapes and land-cover types on the Earth’s surface. Therefore, there are domain distribution gaps between labeled and unlabeled satellite images. To solve this problem, we take the domain shift problem into account for the semi-supervised learning (SSL) network. Feature-level and output-level domain adaptations are applied to reduce the domain distribution gaps between labeled and unlabeled images, thus improving predicted results accuracy of the SSL network. Experimental results on Landsat-8 OLI and GF-1 WFV multispectral images demonstrate that the proposed semi-supervised cloud detection network (SSCDnet) is able to achieve promising cloud detection performance when using a limited number of labeled samples and outperforms several state-of-the-art SSL methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In terms of semi-supervised cloud detection work, efforts are being made to learn a promising cloud detection model via a limited number of pixel-wise labeled images and a large number of unlabeled ones. However, remote sensing images obtained from the same satellite sensor often show a data distribution drift problem due to the different cloud shapes and land-cover types on the Earth’s surface. Therefore, there are domain distribution gaps between labeled and unlabeled satellite images. To solve this problem, we take the domain shift problem into account for the semi-supervised learning (SSL) network. Feature-level and output-level domain adaptations are applied to reduce the domain distribution gaps between labeled and unlabeled images, thus improving predicted results accuracy of the SSL network. Experimental results on Landsat-8 OLI and GF-1 WFV multispectral images demonstrate that the proposed semi-supervised cloud detection network (SSCDnet) is able to achieve promising cloud detection performance when using a limited number of labeled samples and outperforms several state-of-the-art SSL methods. |
![]() | Saha, Sudipan; Gawlikowski, Jakob; Zhu, Xiao Xiang Fusing multiple untrained networks for hyperspectral change detection Inproceedings XXIV ISPRS Congress 2022, 2022. BibTeX | Tags: @inproceedings{Saha2022a,
title = {Fusing multiple untrained networks for hyperspectral change detection},
author = {Sudipan Saha and Jakob Gawlikowski and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {XXIV ISPRS Congress 2022},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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![]() | Zhao, Shan; Saha, Sudipan; Zhu, Xiao Xiang Graph neural network based open-set domain adaptation Inproceedings XXIV ISPRS Congress 2022, 2022. BibTeX | Tags: @inproceedings{Zhao2022,
title = {Graph neural network based open-set domain adaptation},
author = {Shan Zhao and Sudipan Saha and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {XXIV ISPRS Congress 2022},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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![]() | Saha, Sudipan; Zhan, Shao; Shahzad, Muhammad; Zhu, Xiao Xiang Mitigating distribution shift for multi-sensor classification Inproceedings IEEE Geoscience and Remote Sensing Symposium 2022, IEEE, 2022. BibTeX | Tags: @inproceedings{Saha2022bb,
title = {Mitigating distribution shift for multi-sensor classification},
author = {Sudipan Saha and Shao Zhan and Muhammad Shahzad and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2022},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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![]() | Gawlikowski, Jakob; Saha, Sudipan; Niebling, Julia; Zhu, Xiao Xiang Robust Distribution-Shift Aware SAR-Optical Data Fusion for Multi-Label Scene Classification Inproceedings IEEE Geoscience and Remote Sensing Symposium 2022, IEEE, 2022. BibTeX | Tags: @inproceedings{Gawlikowski2022a,
title = {Robust Distribution-Shift Aware SAR-Optical Data Fusion for Multi-Label Scene Classification},
author = {Jakob Gawlikowski and Sudipan Saha and Julia Niebling and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2022},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Hermann, Martin; Saha, Sudipan; Zhu, Xiao Xiang Few-Shot Filtering for the Detection of Specialized Change in Remote Sensing Inproceedings ICLR 2022 Workshop on Practical ML for Developing Countries, 2022. BibTeX | Tags: @inproceedings{Hermann2022,
title = {Few-Shot Filtering for the Detection of Specialized Change in Remote Sensing},
author = {Martin Hermann and Sudipan Saha and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {ICLR 2022 Workshop on Practical ML for Developing Countries},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Li, Qingyu; Shi, Yilei; Zhu, Xiao Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training 2022. Links | BibTeX | Tags: @unknown{unknown,
title = {Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training},
author = {Qingyu Li and Yilei Shi and Xiao Zhu},
doi = {10.48550/arXiv.2205.08416},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {unknown}
}
|
![]() | Diaconu, Codruț-Andrei; Saha, Sudipan; Günnemann, Stephan; Zhu, Xiao Xiang Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model Inproceedings CVPR 2022 Workshop Earthvision, 2022. BibTeX | Tags: @inproceedings{Diaconu2022,
title = {Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model},
author = {Codruț-Andrei Diaconu and Sudipan Saha and Stephan Günnemann and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {CVPR 2022 Workshop Earthvision},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Gu, Ziqi; Ebel, Patrick; Yuan, Qiangqiang; Schmitt, Michael; Zhu, Xiao Xiang Explicit Haze & Cloud Removal for Global Land Cover Classification Inproceedings CVPR Workshops, pp. 1–6, 2022. BibTeX | Tags: @inproceedings{gu2022explicithcr,
title = {Explicit Haze & Cloud Removal for Global Land Cover Classification},
author = {Ziqi Gu and Patrick Ebel and Qiangqiang Yuan and Michael Schmitt and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {CVPR Workshops},
pages = {1--6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Saha, Sudipan; Zhao, Shan; Sheikh, Nasrullah; Zhu, Xiao Xiang Reiterative Domain Aware Multi-Target Adaptation Inproceedings German Conference on Pattern Recognition (GCPR), 2022. BibTeX | Tags: @inproceedings{Saha2022d,
title = {Reiterative Domain Aware Multi-Target Adaptation},
author = {Sudipan Saha and Shan Zhao and Nasrullah Sheikh and Xiao Xiang Zhu},
year = {2022},
date = {2022-01-01},
booktitle = {German Conference on Pattern Recognition (GCPR)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | M., Mou Song Zhu Saha Shahzad L Q X S Unsupervised Single-Scene Semantic Segmentation for Earth Observation Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2022. BibTeX | Tags: @article{Saha2022cb,
title = {Unsupervised Single-Scene Semantic Segmentation for Earth Observation},
author = {Mou Song Zhu L Q X Saha S. Shahzad M.},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2021
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![]() | Usmani, Fehmida; Khan, Ihtesham; Siddiqui, Mehek; Khan, Mahnoor; Bilal, Muhammad; Masood, M; Ahmad, Arsalan; Shahzad, Muhammad; Curri, Vittorio Cross-feature trained machine learning models for QoT-estimation in optical networks Journal Article Optical Engineering, 60 (12), pp. 125106, 2021. Links | BibTeX | Tags: @article{Usmani2021,
title = {Cross-feature trained machine learning models for QoT-estimation in optical networks},
author = {Fehmida Usmani and Ihtesham Khan and Mehek Siddiqui and Mahnoor Khan and Muhammad Bilal and M Masood and Arsalan Ahmad and Muhammad Shahzad and Vittorio Curri},
doi = {10.1117/1.oe.60.12.125106},
year = {2021},
date = {2021-12-03},
journal = {Optical Engineering},
volume = {60},
number = {12},
pages = {125106},
publisher = {SPIE-Intl Soc Optical Eng},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Bloice, Marcus D; Roth, Peter M; Holzinger, Andreas Performing Arithmetic Using a Neural Network Trained on Images of Digit Permutation Pairs Journal Article J. Intell. Inf. Syst., 57 (3), pp. 547–562, 2021, ISSN: 0925-9902. Abstract | Links | BibTeX | Tags: @article{10.1007/s10844-021-00662-9,
title = {Performing Arithmetic Using a Neural Network Trained on Images of Digit Permutation Pairs},
author = {Marcus D Bloice and Peter M Roth and Andreas Holzinger},
url = {https://doi.org/10.1007/s10844-021-00662-9},
doi = {10.1007/s10844-021-00662-9},
issn = {0925-9902},
year = {2021},
date = {2021-12-01},
journal = {J. Intell. Inf. Syst.},
volume = {57},
number = {3},
pages = {547–562},
publisher = {Kluwer Academic Publishers},
address = {USA},
abstract = {In this paper, a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images consisting of two side-by-side handwritten digits, where the image’s label is the summation of the two digits contained in the combined image. Crucially, the network was tested on permutation pairs that were not present during training in an effort to see if the network could learn the task of addition, as opposed to simply mapping images to labels. A dataset was generated for all possible permutation pairs of length 2 for the digits 0–9 using MNIST as a basis for the images, with one thousand samples generated for each permutation pair. For testing the network, samples generated from previously unseen permutation pairs were fed into the trained network, and its predictions measured. Results were encouraging, with the network achieving an accuracy of over 90% on some permutation train/test splits. This suggests that the network learned at first digit recognition, and subsequently the further task of addition based on the two recognised digits. As far as the authors are aware, no previous work has concentrated on learning a mathematical operation in this way. This paper is an attempt to demonstrate that a network can learn more than a direct mapping from image to label, but is learning to analyse two separate regions of an image and combining what was recognised to produce the final output label.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images consisting of two side-by-side handwritten digits, where the image’s label is the summation of the two digits contained in the combined image. Crucially, the network was tested on permutation pairs that were not present during training in an effort to see if the network could learn the task of addition, as opposed to simply mapping images to labels. A dataset was generated for all possible permutation pairs of length 2 for the digits 0–9 using MNIST as a basis for the images, with one thousand samples generated for each permutation pair. For testing the network, samples generated from previously unseen permutation pairs were fed into the trained network, and its predictions measured. Results were encouraging, with the network achieving an accuracy of over 90% on some permutation train/test splits. This suggests that the network learned at first digit recognition, and subsequently the further task of addition based on the two recognised digits. As far as the authors are aware, no previous work has concentrated on learning a mathematical operation in this way. This paper is an attempt to demonstrate that a network can learn more than a direct mapping from image to label, but is learning to analyse two separate regions of an image and combining what was recognised to produce the final output label. |
![]() | Yuan, Zhenghang; Mou, Lichao; Zhu, Xiao Xiang Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data Conference IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Institute of Electrical and Electronics Engineers (IEEE), 2021, ISBN: 9781665403696. Abstract | Links | BibTeX | Tags: @conference{Yuan2021b,
title = {Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data},
author = {Zhenghang Yuan and Lichao Mou and Xiao Xiang Zhu},
doi = {10.1109/IGARSS47720.2021.9553624},
isbn = {9781665403696},
year = {2021},
date = {2021-10-01},
booktitle = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)},
journal = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)},
pages = {2999-3002},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
abstract = {Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance. |
![]() | Bloice, M D; Roth, P M; Holzinger, A Performing arithmetic using a neural network trained on images of digit permutation pairs Journal Article Journal of Intelligent Information Systems, 57 , pp. 547–562, 2021. Links | BibTeX | Tags: . @article{Bloice2021,
title = {Performing arithmetic using a neural network trained on images of digit permutation pairs},
author = {Bloice, M.D. and Roth, P.M. and Holzinger, A.},
url = {https://link.springer.com/article/10.1007/s10844-021-00662-9},
doi = {https://doi.org/10.3390/jimaging7020021 },
year = {2021},
date = {2021-08-06},
journal = {Journal of Intelligent Information Systems},
volume = {57},
pages = {547–562},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Perko, Roland; Almer, Alexander; Theuermann, Mario; Klopschitz, Manfred; Schnsbel, Thomas; Roth, Peter Protocol Design Issues for Object Density Estimation and Counting in Remote Sensing Book Chapter 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 2021. Links | BibTeX | Tags: @inbook{Perko2021b,
title = {Protocol Design Issues for Object Density Estimation and Counting in Remote Sensing},
author = {Roland Perko and Alexander Almer and Mario Theuermann and Manfred Klopschitz and Thomas Schnsbel and Peter Roth},
doi = {10.1109/igarss47720.2021.9553934},
year = {2021},
date = {2021-07-11},
booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
![]() | Hua, Yuansheng; Mou, Lichao; Jin, Pu; Zhu, Xiao Unconstrained Aerial Scene Recognition with Deep Neural Networks and a New Dataset Book Chapter 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 11–16, IEEE, Brussels, Belgium, 2021. Links | BibTeX | Tags: @inbook{Hua2021a,
title = {Unconstrained Aerial Scene Recognition with Deep Neural Networks and a New Dataset},
author = {Yuansheng Hua and Lichao Mou and Pu Jin and Xiao Zhu},
doi = {10.1109/igarss47720.2021.9554633},
year = {2021},
date = {2021-07-11},
booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
pages = {11--16},
publisher = {IEEE},
address = {Brussels, Belgium},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
![]() | Perwaiz, Nazia; Fraz, Muhammad; Shahzad, Muhammad Stochastic attentions and context learning for person re-identification Journal Article PeerJ Computer Science, 7 , pp. e447, 2021. Links | BibTeX | Tags: @article{Perwaiz2021,
title = {Stochastic attentions and context learning for person re-identification},
author = {Nazia Perwaiz and Muhammad Fraz and Muhammad Shahzad},
doi = {10.7717/peerj-cs.447},
year = {2021},
date = {2021-05-05},
journal = {PeerJ Computer Science},
volume = {7},
pages = {e447},
publisher = {PeerJ},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Hua, Yuansheng; Mou, Lichao; Lin, Jianzhe; Heidler, Konrad; Zhu, Xiao Xiang Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 2021. BibTeX | Tags: @article{hua2021prototype,
title = {Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks},
author = {Yuansheng Hua and Lichao Mou and Jianzhe Lin and Konrad Heidler and Xiao Xiang Zhu},
year = {2021},
date = {2021-04-09},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Klemmer, Konstantin; Saha, Sudipan; Kahl, Matthias; Xu, Tianlin; Zhu, Xiao Xiang Generative modeling of spatio-temporal weather patterns with extreme event conditioning Inproceedings AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021, 2021. Abstract | Links | BibTeX | Tags: @inproceedings{Klemmer2021,
title = {Generative modeling of spatio-temporal weather patterns with extreme event conditioning},
author = {Konstantin Klemmer and Sudipan Saha and Matthias Kahl and Tianlin Xu and Xiao Xiang Zhu},
url = {http://arxiv.org/abs/2104.12469},
year = {2021},
date = {2021-04-01},
booktitle = {AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021},
abstract = {Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data. |
![]() | Hua, Yuansheng; Mou, Lichao; Lin, Jianzhe; Heidler, Konrad; Zhu, Xiao Xiang Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 177 , pp. 89-102, 2021, ISSN: 0924-2716. Abstract | Links | BibTeX | Tags: @article{Hua2021d,
title = {Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks},
author = {Yuansheng Hua and Lichao Mou and Jianzhe Lin and Konrad Heidler and Xiao Xiang Zhu},
url = {https://www.sciencedirect.com/science/article/pii/S0924271621001015},
doi = {10.1016/j.isprsjprs.2021.04.006},
issn = {0924-2716},
year = {2021},
date = {2021-04-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {177},
pages = {89-102},
abstract = {A R T I C L E I N F O Keywords: Convolutional neural network (CNN) Multi-scene recognition in single images Memory network Multi-scene aerial image dataset Multi-head attention-based memory retrieval Prototype learning A B S T R A C T Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time-and labor-consuming. To address this, we propose a prototype-based memory network to recognize multiple scenes in a single image by leveraging massive well-annotated single-scene images. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory. Afterwards, a multi-head attention-based memory retrieval module is devised to retrieve scene prototypes relevant to query multi-scene images for final predictions. Notably, only a limited number of annotated multi-scene images are needed in the training phase. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset. Experimental results on variant dataset configurations demonstrate the effectiveness of our network. Our dataset and codes are publicly available 1 .},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A R T I C L E I N F O Keywords: Convolutional neural network (CNN) Multi-scene recognition in single images Memory network Multi-scene aerial image dataset Multi-head attention-based memory retrieval Prototype learning A B S T R A C T Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time-and labor-consuming. To address this, we propose a prototype-based memory network to recognize multiple scenes in a single image by leveraging massive well-annotated single-scene images. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory. Afterwards, a multi-head attention-based memory retrieval module is devised to retrieve scene prototypes relevant to query multi-scene images for final predictions. Notably, only a limited number of annotated multi-scene images are needed in the training phase. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset. Experimental results on variant dataset configurations demonstrate the effectiveness of our network. Our dataset and codes are publicly available 1 . |
![]() | Perko, Roland; Klopschitz, Manfred; Almer, Alexander; Roth, Peter M Critical Aspects of Person Counting and Density Estimation Journal Article Journal of Imaging 2021, Vol. 7, Page 21, 7 (2), pp. 21, 2021, ISSN: 2313-433X. Abstract | Links | BibTeX | Tags: @article{Perko2021b,
title = {Critical Aspects of Person Counting and Density Estimation},
author = {Roland Perko and Manfred Klopschitz and Alexander Almer and Peter M Roth},
url = {https://www.mdpi.com/2313-433X/7/2/21/htm https://www.mdpi.com/2313-433X/7/2/21},
doi = {10.3390/JIMAGING7020021},
issn = {2313-433X},
year = {2021},
date = {2021-01-31},
journal = {Journal of Imaging 2021, Vol. 7, Page 21},
volume = {7},
number = {2},
pages = {21},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols. |
 | Perko, Roland; Klopschitz, Manfred; Almer, Alexander; Roth, Peter M Critical Aspects of Person Couting and Density Estimation Journal Article Journal of Imaging, 7 (2), pp. 21, 2021. BibTeX | Tags: @article{perko21a,
title = {Critical Aspects of Person Couting and Density Estimation},
author = {Roland Perko and Manfred Klopschitz and Alexander Almer and Peter M Roth},
year = {2021},
date = {2021-01-01},
journal = {Journal of Imaging},
volume = {7},
number = {2},
pages = {21},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Basirat, Mina; Roth, Peter M S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization Inproceedings International Conf. on Computer Vision Theory and Applications, 2021. BibTeX | Tags: @inproceedings{basirat21ab,
title = {S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization},
author = {Mina Basirat and Peter M Roth},
year = {2021},
date = {2021-01-01},
booktitle = {International Conf. on Computer Vision Theory and Applications},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
 | Editors:Camps-Valls, Gustau; Tuia, Devis; Zhu, Xiao Xiang; Reichstein, Markus Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences Book Wiley & Sons, 2021, ISBN: 978-1-119-64614-3. BibTeX | Tags: @book{CampsValls21wiley,
title = {Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences},
author = {Gustau Editors:Camps-Valls and Devis Tuia and Xiao Xiang Zhu and Markus Reichstein },
isbn = {978-1-119-64614-3},
year = {2021},
date = {2021-01-01},
publisher = {Wiley & Sons},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
 | Mou, Lichao; Saha, Sudipan; Hua, Yuansheng; Bovolo, Francesca; Bruzzone, Lorenzo; Zhu, Xiao Xiang Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{deepReinforcementTgrs2021,
title = {Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification},
author = {Lichao Mou and Sudipan Saha and Yuansheng Hua and Francesca Bovolo and Lorenzo Bruzzone and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Saha, Sudipan; Banerjee, Biplab; Zhu, Xiao Xiang Trusting Small Training Dataset for Supervised Change Detection Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{trustingSmallDatasetSudipanIgarss2021,
title = {Trusting Small Training Dataset for Supervised Change Detection},
author = {Sudipan Saha and Biplab Banerjee and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Prexl, Jonathan; Saha, Sudipan; Zhu, Xiao Xiang Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{mitigatingJonathanIgarss2021,
title = {Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning},
author = {Jonathan Prexl and Sudipan Saha and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Towards Out-of-distribution Detection for Remote Sensing Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{towardsGawlikowskiIgarss2021,
title = {Towards Out-of-distribution Detection for Remote Sensing},
author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Saha, Sudipan; Kondmann, Lukas; Zhu, Xiao Xiang Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images Conference XXIV ISPRS Congress 2021, 2021. BibTeX | Tags: @conference{deepNoLearningSudipanIsprs2021,
title = {Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images},
author = {Sudipan Saha and Lukas Kondmann and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {XXIV ISPRS Congress 2021},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Ebel, Patrick; Saha, Sudipan; Zhu, Xiao Xiang Fusing Multi-modal Data for Supervised Change Detection Conference XXIV ISPRS Congress 2021, 2021. BibTeX | Tags: @conference{fusingPatrickIsprs2021,
title = {Fusing Multi-modal Data for Supervised Change Detection},
author = {Patrick Ebel and Sudipan Saha and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {XXIV ISPRS Congress 2021},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Nandy, Jay; Saha, Sudipan; Hsu, Wynne; Lee, Mong Li; Zhu, Xiao Xiang Covariate Shift Adaptation for Adversarially Robust Classifier Conference ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021. BibTeX | Tags: @conference{covariateJayIclrW2021,
title = {Covariate Shift Adaptation for Adversarially Robust Classifier},
author = {Jay Nandy and Sudipan Saha and Wynne Hsu and Mong Li Lee and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {ICLR 2021 Workshop on Security and Safety in Machine Learning Systems},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Out-of-distribution Detection in Satellite Image Classification Conference RobustML workshop at ICLR 2021, 2021. BibTeX | Tags: @conference{oodJakobIclrW2021,
title = {Out-of-distribution Detection in Satellite Image Classification},
author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {RobustML workshop at ICLR 2021},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Kochupillai, Mrinalini; Gallersdörfer, Ulrich; Köninger, Julia; Beck, Roman Incentivizing research & innovation with agrobiodiversity conserved in situ: Possibilities and limitations of a blockchain-based solution Journal Article Journal of Cleaner Production, 309 , pp. 127155, 2021, ISSN: 0959-6526. BibTeX | Tags: @article{RN2468,
title = {Incentivizing research & innovation with agrobiodiversity conserved in situ: Possibilities and limitations of a blockchain-based solution},
author = {Mrinalini Kochupillai and Ulrich Gallersdörfer and Julia Köninger and Roman Beck},
issn = {0959-6526},
year = {2021},
date = {2021-01-01},
journal = {Journal of Cleaner Production},
volume = {309},
pages = {127155},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Kochupillai, M Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data: Legal and Ethical Considerations of an AI and Blackchain Based Solution, Conference paper, Towards Responsible Plant Data Linkage: Global Challenges for Food Security and Governance, Alan Turing Institute and University of Exeter, March 2021 (forthcoming in an edited volume with Springer Nature) Conference 2021. BibTeX | Tags: @conference{RN2474b,
title = {Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data: Legal and Ethical Considerations of an AI and Blackchain Based Solution, Conference paper, Towards Responsible Plant Data Linkage: Global Challenges for Food Security and Governance, Alan Turing Institute and University of Exeter, March 2021 (forthcoming in an edited volume with Springer Nature)},
author = {M Kochupillai},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
 | Saha, Sudipan; Ebel, Patrick; Zhu, Xiao Xiang Self-supervised multisensor change detection Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{selfSupervisedMultisensorTgrs2021,
title = {Self-supervised multisensor change detection},
author = {Sudipan Saha and Patrick Ebel and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Saha, Sudipan; Ahmad, Tahir Federated transfer learning: Concept and applications Journal Article Intelligenza Artificiale, 15 (1), pp. 35–44, 2021. BibTeX | Tags: @article{ftlSurvey2021,
title = {Federated transfer learning: Concept and applications},
author = {Sudipan Saha and Tahir Ahmad},
year = {2021},
date = {2021-01-01},
journal = {Intelligenza Artificiale},
volume = {15},
number = {1},
pages = {35--44},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Saha, Sudipan; Zhu, Xiao Xiang Patch-level unsupervised planetary change detection Journal Article IEEE Geoscience and Remote Sensing Letters, 2021. BibTeX | Tags: @article{Saha2021,
title = {Patch-level unsupervised planetary change detection},
author = {Sudipan Saha and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Saha, Sudipan; Mou, Lichao; Shahzad, Muhammad; Zhu, Xiao Xiang Segmentation of VHR EO Images using Unsupervised Learning Inproceedings 2021. BibTeX | Tags: @inproceedings{Saha2021b,
title = {Segmentation of VHR EO Images using Unsupervised Learning},
author = {Sudipan Saha and Lichao Mou and Muhammad Shahzad and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {ECML PKDD 2021 workshop Machine Learning for Earth Observation (MACLEAN)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Ahmed, Nouman; Saha, Sudipan; Mohsin, Maaz; Shahzad, Muhammad; Zhu, Xiao Xiang Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image Inproceedings 2021. BibTeX | Tags: @inproceedings{Ahmed2021,
title = {Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image},
author = {Nouman Ahmed and Sudipan Saha and Maaz Mohsin and Muhammad Shahzad and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {ICCV 2021 workshop on Learning to Understand Aerial Images (LUAI)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
![]() | Saha, Sudipan; Kondmann, Lukas; Song, Qian; Zhu, Xiao Xiang Change Detection in Hyperdimensional Images using Untrained Models Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. BibTeX | Tags: @article{Saha2021c,
title = {Change Detection in Hyperdimensional Images using Untrained Models},
author = {Sudipan Saha and Lukas Kondmann and Qian Song and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Kondmann, Lukas; Toker, Aysim; Saha, Sudipan; Schölkopf, Bernhard; Leal-Taixé, Laura; Zhu, Xiao Xiang Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images Journal Article arXiv preprint arXiv:2110.02068, 2021. BibTeX | Tags: @article{Kondmann2021,
title = {Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images},
author = {Lukas Kondmann and Aysim Toker and Sudipan Saha and Bernhard Schölkopf and Laura Leal-Taixé and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2110.02068},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
![]() | Yuan, Zhenghang; Mou, Lichao; Zhu, Xiao Xiang Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data Conference IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) , Institute of Electrical and Electronics Engineers (IEEE), 2021, ISBN: 9781665403696. Abstract | Links | BibTeX | Tags: @conference{Yuan2021,
title = {Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data},
author = {Zhenghang Yuan and Lichao Mou and Xiao Xiang Zhu},
doi = {10.1109/IGARSS47720.2021.9553624},
isbn = {9781665403696},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) },
journal = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) },
pages = {2999-3002},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
abstract = {Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance. |
