Results 51 to 60 of about 157,621 (267)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation.
Yongheng Zhao +3 more
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Hyperparameter optimisation for Capsule Networks [PDF]
EAI Endorsed Transactions on Cloud Systems, 2019 Convolutional Neural Networks and its contemporary variants have proven to be ruling benchmarks for most image processing tasks but resort to pooling techniques and routing mechanisms that affect classification accuracy and lose spatial relationship information between involved data points.
Gagana B, S Natarajan
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Hierarchical Graph Capsule Network
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph representation which plays an important role in graph classification.
Jinyu Yang +6 more
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Towards Efficient Capsule Networks
2022 IEEE International Conference on Image Processing (ICIP), 2022 From the moment Neural Networks dominated the scene for image processing, the computational complexity needed to solve the targeted tasks skyrocketed: against such an unsustainable trend, many strategies have been developed, ambitiously targeting performance's preservation.
Renzulli, Riccardo, Grangetto, Marco
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Road marking extraction in UAV imagery using attentive capsule feature pyramid network
International Journal of Applied Earth Observations and Geoinformation, 2022 Accurately and precisely delineating road-markings from very high spatial resolution unmanned aerial vehicle (UAV) images face many challenges, such as complex scenarios, diverse road marking sizes and shapes, and absent and occluded road markings.
Haiyan Guan +6 more
doaj +1 more source
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
, 2018 In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more ...
Almalioglu, Yasin +6 more
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IET Renewable Power GenerationThis paper deals with a new continuous mixed p‐norm (CMPN) based adaptive asymmetrical fuzzy logic controller (AAFLC) method for single stage grid integrated photovoltaic (PV) system as an inverter control strategy.
Neeraj Priyadarshi +3 more
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Capsule Network Implementation On FPGA
مجلة العلوم البحتة والتطبيقية, 2020 A capsule neural network (CapsNet) is a new approach in artificial neural network (ANN) that produces a better hierarchical relationship. The performance of CapsNet on graphics processing unit (GPU) is considerably better than convolutional neural ...
Salim A. Adrees , Ala A. Abdulrazeg
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Multi-labeled Relation Extraction with Attentive Capsule Network
, 2018 To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly
Jia, Weijia +3 more
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TI-Capsule: Capsule Network for Stock Exchange Prediction
CoRR, 2021 Today, the use of social networking data has attracted a lot of academic and commercial attention in predicting the stock market. In most studies in this area, the sentiment analysis of the content of user posts on social networks is used to predict market fluctuations. Predicting stock marketing is challenging because of the variables involved. In the
Ramin Mousa +5 more
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