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Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network

Juepeng Zheng, Haohuan Fu, Weijia Li, Wenzhao Wu, Yi Zhao, Runmin Dong, Le Yu
2020 ISPRS journal of photogrammetry and remote sensing (Print)  
In this paper, we propose a novel domain adaptive oil palm tree detection method, i.e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection  ...  Second, we embedded a multi-level attention mechanism (MLA) into our architecture for enhancing the transferability, including a feature level attention and an entropy level attention.  ...  Conclusions In this paper, we propose a novel domain adaptive oil palm tree counting and detection method, i.e., a Multi-level Attention Domain Adaptation Network (MADAN).  ... 
doi:10.1016/j.isprsjprs.2020.07.002 fatcat:crduv7xd3zetxohsleeh3twnhm

Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing [article]

Zhentao Xia, Likai Wang, Weiguang Qu, Junsheng Zhou, Yanhui Gu
2019 arXiv   pre-print
In addition, to adapt three dif-ferent domains, we utilize neural network based deep transfer learning which transfers the pre-trained partial network in the source domain to be a part of deep neural network  ...  Our system is based on the stack-pointer networks(STACKPTR).  ...  We model it as a domain adaptation problem, where we are given one source domain and three target domains, and the core task is to adapt a dependency parser trained on the source domain to the target domain  ... 
arXiv:1908.02895v1 fatcat:4cb2dmbgubfdhlcybqh22olvwi

Multi-source Attention for Unsupervised Domain Adaptation [article]

Xia Cui, Danushka Bollegala
2020 arXiv   pre-print
However, it is challenging to select the appropriate source(s) for classifying a given target instance in multi-source unsupervised domain adaptation (UDA).  ...  Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain.  ...  Multi-Source Domain Attention Let us assume that are given N source domains, S 1 , S 2 , . . . , S N , and required to adapt to a target domain T .  ... 
arXiv:2004.06608v2 fatcat:mazmqmek3jesdi6wxgdhbns3z4

Multi-Source Distilling Domain Adaptation

Sicheng Zhao, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates  ...  Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).  ...  Multi-source Distilling Domain Adaptation In this section, we introduce the proposed multi-source distilling domain adaptation (MDDA) network.  ... 
doi:10.1609/aaai.v34i07.6997 fatcat:qwetkmhp4vflfdxkif2wpwq36a

Multi-source Distilling Domain Adaptation [article]

Sicheng Zhao, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
2020 arXiv   pre-print
In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates  ...  Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).  ...  Multi-source Distilling Domain Adaptation In this section, we introduce the proposed multi-source distilling domain adaptation (MDDA) network.  ... 
arXiv:1911.11554v2 fatcat:cjenajzm7vd4znxptnxdlqsc4y

Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation [article]

Yiming Xu, Lin Chen, Zhongwei Cheng, Lixin Duan, Jiebo Luo
2019 arXiv   pre-print
In this paper, we tackle the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities.  ...  of the source and target domains.  ...  We have presented a novel supervised multi-modal domain adaptation framework for open-ended visual question answering.  ... 
arXiv:1911.04058v1 fatcat:qskjkhoy5jespf3qj4xuev4z6a

Transformer Based Multi-Source Domain Adaptation [article]

Dustin Wright, Isabelle Augenstein
2020 arXiv   pre-print
Here, we investigate the problem of unsupervised multi-source domain adaptation, where a model is trained on labelled data from multiple source domains and must make predictions on a domain for which no  ...  Additionally, we show that mixture of experts leads to significant performance improvements by comparing several variants of mixing functions, including one novel mixture based on attention.  ...  https://github.com/copenlu/ xformer-multi-source-domain-adaptation  ... 
arXiv:2009.07806v1 fatcat:7jhd2qr63nfsrigt2guvf2fbga

Multi-adversarial Partial Transfer Learning with Object-level Attention Mechanism for Unsupervised Remote Sensing Scene Classification

Peng Li, Dezheng Zhang, Peng Chen, Xin Liu, Aziguli Wulamu
2020 IEEE Access  
INDEX TERMS Partial transfer learning, domain adaption, object-level attention, remote sensing scene classification, multi-adversarial learning, convolutional neural networks. 56650 This work is licensed  ...  In this context, Multi-adversarial Object-level Attention Network (MOAN) is proposed for partial transfer learning and selecting useful features.  ...  learning-based domain adaption.  ... 
doi:10.1109/access.2020.2982034 fatcat:3exnuyctpvdzjgpj6alwmk7soi

Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery [article]

Mauro Martini, Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
2021 arXiv   pre-print
In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention  ...  Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain  ...  In particular, we perform a thorough analysis of domain adaptation applied to challenging multispectral, multi-temporal data, highlighting the advantages of adapting state-of-the-art self-attention-based  ... 
arXiv:2104.00564v2 fatcat:sbbzkzjibzbbxp2sza4qzpkini

Semi-supervised fine-grained image categorization using transfer learning with hierarchical multi-scale adversarial networks

Peng Chen, Peng Li, Qing Lia, Dezheng Zhang
2019 IEEE Access  
We call the proposed hierarchical framework "Attentional Multi-Adversarial Networks (AMAN)".  ...  In order to exploit useful local features, a novel adaptive attention mechanism, Region Adversarial Network (RAN) which can select attention regions during adversarial learning and generate valuable fine-grained  ...  adaption approaches are based on one assumption: the categories in target domain are same to those in source domain.  ... 
doi:10.1109/access.2019.2934476 fatcat:stu53kvrs5gc3ortvtmijywpda

Deep visual unsupervised domain adaptation for classification tasks: a survey

Yeganeh Madadi, Vahid Seydi, Kamal Nasrollahi, Reshad Hosseini, Thomas B. Moeslund
2020 IET Image Processing  
five groups of discrepancy-, adversarial-, reconstruction-, representation-, and attention-based methods.  ...  To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used.  ...  [28] proposed a moment matching approach, M 3 SDA, for multi-source domain adaptation models which not only aligns the source domains with target domain but also source domain with each other simultaneously  ... 
doi:10.1049/iet-ipr.2020.0087 fatcat:x7v5et3r6nagpe2ivuu5nd4qku

Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment [article]

Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Min Wu, Rui Zhao, Zhenghua Chen
2021 arXiv   pre-print
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.  ...  When considering videos, the negative transfer would be provoked by spatial-temporal features and can be formulated into a more challenging Multi-Source Video Domain Adaptation (MSVDA) problem.  ...  The trade-off weight for the moment-based spatial and temporal feature discrepancies λ df and λ dt are set to 0.005 and 0.01. All experiments are conducted using two NVIDIA RTX 2080 Ti GPUs.  ... 
arXiv:2109.09964v2 fatcat:qaozikkiqjagtas544pajv4dge

Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

Mauro Martini, Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
2021 Remote Sensing  
In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based  ...  Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain  ...  In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multitemporal data, highlighting the advantages of adapting state-of-the-art self-attention-based  ... 
doi:10.3390/rs13132564 fatcat:geomb62lxbeqpobzztx4dlq4cm

MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation [article]

Sicheng Zhao, Bo Li, Xiangyu Yue, Pengfei Xu, Kurt Keutzer
2020 arXiv   pre-print
Since the labeled data may be collected from multiple sources, multi-source domain adaptation (MDA) has attracted increasing attention.  ...  Specifically, we design an end-to-end Multi-source Adversarial Domain Aggregation Network (MADAN).  ...  Multi-source Domain Adaptation Multi-source domain adaptation (MDA) considers a more practical scenario, where the training data are collected from multiple sources [29, 48] .  ... 
arXiv:2003.00820v1 fatcat:p3p2jyvurrb2zp4fefguunezp4

Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling [article]

Ouyu Lan, Xiao Huang, Bill Yuchen Lin, He Jiang, Liyuan Liu, Xiang Ren
2020 arXiv   pre-print
We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation.  ...  It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module.  ...  Acknowledgements This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via Contract  ... 
arXiv:1910.04289v2 fatcat:fbum7w36evajzow7lhwf4fup6m
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