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Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information [article]

Lingfeng Yang, Xiang Li, Renjie Song, Borui Zhao, Juntian Tao, Shihao Zhou, Jiajun Liang, Jian Yang
2022 arXiv   pre-print
To our best knowledge, it is the first attempt to explore the idea of dynamic networks to exploit multimodal information in fine-grained image classification tasks.  ...  The dynamic MLP is an efficient structure parameterized by the learned embeddings of variable locations and dates.  ...  ., the dynamic MLP, to improve the effectiveness of geographical and temporal information on fine-grained image classification.  ... 
arXiv:2203.03253v1 fatcat:dh5pewztt5bhjfyj5vluxjp47q

InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping [article]

Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen, Dong Li, Kefeng Deng, Qiang Wu
2021 arXiv   pre-print
Typically, the risk can be identified by jointly considering product content (e.g., title and image) and seller behavior.  ...  However, it is intractable for commodity GPUs because the time and memory required by Transformer grow quadratically with the increase of sequence length.  ...  Fine-grained prediction task is respectively exerted on 1 and 2 . We also leverage contrastive learning to design the training objective.  ... 
arXiv:2106.06905v1 fatcat:qfngohw5vzcp7cawxyhvewp45y

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Yue Zhu, Christian Geiß, Emily So
2021 International Journal of Applied Earth Observation and Geoinformation  
Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification.  ...  i.e., leveraging Sentinel-2 images for generating SR Landsat images.  ...  Identifying a research gap, this paper aims to address the limitation of low-resolution historic RS images on fine-grained multi-temporal LULC classification through leveraging newly produced data.  ... 
doi:10.1016/j.jag.2021.102543 fatcat:h6uku6sdgndc5hv6ny5q7kmvri

Activation Regression for Continuous Domain Generalization with Applications to Crop Classification [article]

Samar Khanna, Bram Wallace, Kavita Bala, Bharath Hariharan
2022 arXiv   pre-print
We develop a dataset spatially distributed across the entire continental United States, providing macroscopic insight into the effects of geography on crop classification in multi-spectral and temporally  ...  Combined, we provide a novel perspective on geographic generalisation in satellite imagery and a simple-yet-effective approach to leverage domain knowledge. Code is available at:  ...  Such censuses yield dense (30m resolution) annotations with fine-grain crop information across the entire country.  ... 
arXiv:2204.07030v1 fatcat:kbu5fnyd45gmjnhdux5jl45rp4

Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation [article]

Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato, Raffaele Gaetano
2020 arXiv   pre-print
The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities  ...  to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich and complex image  ...  ACKNOWLEDGEMENTS This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 (DigitAg), the GEOSUD project with reference ANR  ... 
arXiv:2004.14672v1 fatcat:kmniw5nj45djlhku4ntmp3tfa4

Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

Andrei Stoian, Vincent Poulain, Jordi Inglada, Victor Poughon, Dawa Derksen
2019 Remote Sensing  
However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality  ...  Our contributions include a framework for working with Sentinel-2 L2A time-series image data, an adaptation of the U-Net model (a fully convolutional neural network) for dealing with sparse annotation  ...  The approach used by Theia is described in [8] and uses pixel-based supervised classification, leveraging existing (and therefore, out of date) data bases as annotations for training and all available  ... 
doi:10.3390/rs11171986 fatcat:5bbows3f25e6naoglmkdtnomn4

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video  ...  To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree  ...  Singapore under the Corp Lab@University Scheme, and also supported by the funding awarded to Dr.  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Weakly Supervised Learning for Land Cover Mapping of Satellite Image Time Series via Attention-Based CNN

Dino Ienco, Yawogan Jean Eudes Gbodjo, Raffaele Gaetano, Roberto Interdonato
2020 IEEE Access  
INDEX TERMS Weakly supervised learning, object-based image classification, satellite image time series, land cover classification, deep learning.  ...  Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics.  ...  This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 (DigitAg).  ... 
doi:10.1109/access.2020.3024133 fatcat:eqvqfgtjwvabppprv7u526dxsa

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
2021 Data Science and Engineering  
With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here  ...  ., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges.  ...  [116] consider both spatial information and temporal information for trajectory classification.  ... 
doi:10.1007/s41019-020-00151-z fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xin He, Yuben Qu, Saeed Amirgholipour, Yue Xi, Priyadarsi Nanda, Xiangjian He
2020 CCF Transactions on Pervasive Computing and Interaction  
However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity.  ...  In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.  ...  Acknowledgements This research is supported by the NSF of China  ... 
doi:10.1007/s42486-020-00039-x fatcat:c3c2b3fvpzdqdlxy2ke7ckxlpu

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations  ...  This means that the current measurement 'snapshot' provides the most valuable information for the model to reconstruct the fine-grained counterpart.  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
We complete this survey by pinpointing current challenges and open future directions for research.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics.  ...  Their method reduces the top-5 error by 39.7% and revolutionizes the imaging classification field.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
We complete this survey by pinpointing current challenges and open future directions for research.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience  ...  Fine-grained Measurements MTSR Fig. 11: Illustration of the image super-resolution (SR) prin- ciple (above) and the mobile traffic super-resolution (MTSR) technique (below).  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
Forecasting such events is of great importance for decision-making and resource allocation.  ...  Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems.  ...  OpenStreetMap is a collaborative project that creates a free editable geographic database of the world. 17 In fine-grained crime analysis such as hot spot prediction, geographical information such as road  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approach [article]

Hoang Duy Trinh, Angel Fernandez Gambin, Lorenza Giupponi, Michele Rossi, Paolo Dini
2020 arXiv   pre-print
The automatic classification of applications and services is an invaluable feature for new generation mobile networks.  ...  The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained  ...  The authors of [36] show that a passive eavesdropper is capable of identifying fine grained user activities for Android and iOS mobile apps, by solely inspecting IP headers.  ... 
arXiv:1910.11617v3 fatcat:kboldsdtjva2vjijjdg2dgujsa
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